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arXiv 2025年

作者： Roy, Santanu Singh, Shweta Sahu, Palak Suresh, Ashvath Das, Debashish Computer Science and Engineering Department NIIT University Jaipur India Bangalore India Faculty of Computing Engineering and the Built Environment Birmingham City University United Kingdom

Lung cancer, a severe form of malignant tumor that originates in the tissues of the lungs, can be fatal if not detected in its early stages. It ranks among the top causes of cancer-related mortality worldwide. Detecting lung cancer manually using chest X-Ray image or Computational Tomography (CT) scans image poses significant challenges for radiologists. Hence, there is a need for automatic diagnosis system of lung cancers from radiology images. With the recent emergence of deep learning, particularly through Convolutional Neural Networks (CNNs), the automated detection of lung cancer has become a much simpler task. Nevertheless, numerous researchers have addressed that the performance of conventional CNNs may be hindered due to class imbalance issue, which is prevalent in medical images. In this research work, we have proposed a novel CNN architecture "Multi-Scale Dense Network (MSD-Net)" (trained-from-scratch). The novelties we bring in the proposed model are (I) We introduce novel dense modules in the 4th block and 5th block of the CNN model. We have leveraged 3 depthwise separable convolutional (DWSC) layers, and one 1×1 convolutional layer in each dense module, in order to reduce complexity of the model considerably. (II) Additionally, we have incorporated one skip connection from 3rd block to 5th block and one parallel branch connection from 4th block to Global Average Pooling (GAP) layer. We have utilized dilated convolutional layer (with dilation rate=2) in the last parallel branch in order to extract multi-scale features. Extensive experiments reveal that our proposed model has outperformed latest CNN model ConvNext-Tiny, recent trend Vision Transformer (ViT), Pooling-based ViT (PiT), and other existing models by significant margins. All the codes of these experiments along with its graphs and confusion matrix are provided on a GitHub link: https://***/Singhsshweta/Lung-Cancer-Detection. © 2025, CC BY.

关键词： Lung cancer

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Knowledge Augmented Significant Language Model-Based Chatbot for Explainable Diabetes Mellitus Prediction

Knowledge Augmented Significant Language Model-Based Chatbot...

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International Conference on Ubiquitous Information Management and Communication (IMCOM)

作者： Mazen Elfayoumi Mohamed AbouElazm Omar Mohamed Tamer Abuhmed Shaker El-Sappagh Faculty of Computer Science and Engineering Galala University Egypt Faculty of Computer Science Goldsmith UOL United Kingdom College of Computing and Informatics Sungkyunkwan University South Korea

ISBN: (数字)9798331507817

ISBN: (纸本)9798331507824

This study proposes an innovative diabetes prediction chatbot that utilizes large language models (LLMs) to determine the likelihood of diabetes based on specific patient inputs. Unlike conventional machine learning models and in addition to providing precise, individualized, robust prediction of diabetes augmented by the percentage of its confidence, this chatbot provides detailed text-based explanations for its individual predictions and enhances user's understanding by retrieving and presenting similar cases using case-based reasoning techniques. Utilizing key health indicators such as hypertension status, heart disease presence, smoking history, BMI, HbA1c level, and blood glucose levels, the chatbot not only predicts the presence of diabetes but also educates users about the underlying reasons for each prediction. The system's explanation module promotes transparency and trust in the predictive process. The proposed architecture integrates the retrieval augmented generation (RAG) technique with prompt engineering across multiple LLMs, including Llama 3.1, GPT- 3.5, and Gemma2. This approach augments the chatbot's responses with contextually relevant data from similar past cases, thereby enhancing both relevance and accuracy. Furthermore, RAG minimizes LLM hallucinations while enhancing them with up-to-date, precise, personalized medical information. The objective of this chatbot is to offer healthcare professionals and patients a dependable, educational instrument for early detection of diabetes supported by transparent AI principles. This will have a positive effect on preventative healthcare measures.

关键词： Technological innovation Accuracy Large language models Computational modeling Retrieval augmented generation Medical services Predictive models Chatbots Diabetes Medical diagnostic imaging

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The Evolution of Zero Trust Architecture (ZTA) from Concept to Implementation

arXiv

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arXiv 2025年

作者： Nasiruzzaman, Md. Ali, Maaruf Salam, Iftekhar Miraz, Mahdi H. School of Computing & Data Science Xiamen University Malaysia Malaysia Faculty of Computer Science IT Universiteti Metropolitan Tirana Albania The Doctoral College University of Wales Trinity Saint David Wales United Kingdom School of Computing Faculty of Arts Science & Technology Wrexham University United Kingdom Faculty of Computing Engineering & Science University of South Wales United Kingdom

Zero Trust Architecture (ZTA) is one of the paradigm changes in cybersecurity, from the traditional perimeter-based model to perimeterless. This article studies the core concepts of ZTA, its beginning, a few use cases and future trends. Emphasising the always-verify and least privilege access, some key tenets of ZTA have grown to be integration technologies like Identity Management, Multi-Factor Authentication (MFA) and real-time analytics. ZTA is expected to strengthen cloud environments, education, work environments (including from home) while controlling other risks like lateral movement and insider threats. Despite ZTA’s benefits, it comes with challenges in the form of complexity, performance overhead and vulnerabilities in the control plane. These require phased implementation and continuous refinement to keep up with evolving organisational needs and threat landscapes. Emerging technologies, such as Artificial Intelligence (AI) and Machine Learning (ML) will further automate policy enforcement and threat detection in keeping up with dynamic cyber threats. Copyright © 2025, The Authors. All rights reserved.

关键词： Machine learning

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Case study of performance degradation on the Xen hypervisor-based virtualization related to the native host

Case study of performance degradation on the Xen hypervisor-...

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International Symposium on INFOTEH-JAHORINA (INFOTEH)

作者： Borislav Đorđević Kristina Janjić Nenad Kraljević Nikola Davidović Institute Mihailo Pupin Belgrade Serbia School of Electrical and Computing Engineering of Applied Studies Academy of Technical and Art Applied Studies Belgrade Serbia Faculty of Electrical Engineering University of East Sarajevo BIH School of Electrical and Computing Engineering of Applied Studies Belgrade Serbia

ISBN: (数字)9798331515799

ISBN: (纸本)9798331515805

This paper conducts a comparative analysis of file system performance between a native operating system and Xen hypervisor-based virtualization. The study uses CentOS Stream 9 as both the native and guest operating system and employs Filebench for benchmarking purposes. Performance tests were carried out on the native operating system and within the Xen virtual environment, accommodating one to four virtual machines. A mathematical model was established for both the native and virtual environments, predicting that the native operating system would significantly outperform the virtualized one. The findings of this paper corroborate the model's predictions, demonstrating a substantial decline in file system performance due to virtualization.

关键词： Degradation Virtual machine monitors File systems Operating systems Linux Predictive models Benchmark testing Mathematical models Virtual machines Hardware

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METFORD - Mutation tEsTing Framework fOR anDroid

arXiv

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arXiv 2025年

作者： Vincenzi, Auri M.R. Kuroishi, Pedro H. Bispo, João C.V.M. da Veiga, Ana R.C. da Mata, David R.C. Azevedo, Francisco B. Paiva, Ana C.R. Department of Computing Federal University of São Carlos SP São Carlos Brazil INESC TEC Faculty of Engineering University of Porto Porto Portugal Faculty of Engineering University of Porto Porto Portugal

Mutation testing may be used to guide test case generation and as a technique to assess the quality of test suites. Despite being used frequently, mutation testing is not so commonly applied in the mobile world. One critical challenge in mutation testing is dealing with its computational cost. Generating mutants, running test cases over each mutant, and analyzing the results may require significant time and resources. This research aims to contribute to reducing Android mutation testing costs. It implements mutation testing operators (traditional and Android-specific) according to mutant schemata (implementing multiple mutants into a single code file). It also describes an Android mutation testing framework developed to execute test cases and determine mutation scores. Additional mutation operators can be implemented in JavaScript and easily integrated into the framework. The overall approach is validated through case studies showing that mutant schemata have advantages over the traditional mutation strategy (one file per mutant). The results show mutant schemata overcome traditional mutation in all evaluated aspects with no additional cost: it takes 8.50% less time for mutant generation, requires 99.78% less disk space, and runs, on average, 6.45% faster than traditional mutation. Moreover, considering sustainability metrics, mutant schemata have 8,18% less carbon footprint than traditional strategy. © 2025, CC BY-NC-SA.

关键词： Carbon footprint

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Parallel fault diagnosis using hierarchical fuzzy Petri net by reversible and dynamic decomposition mechanism

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Frontiers of Information Technology & Electronic engineering 2025年第1期26卷 93-108页

作者： Yinhong XIANG Kaiqing ZHOU Arezoo SARKHEYLI-HÄGELE Yusliza YUSOFF Diwen KANG Azlan Mohd ZAIN School of Communication and Electronics Engineering Jishou UniversityJishou 416000China Internet of Things and People Research Center Department of Computer Science and Media TechnologyMalmöUniversityMalmö20506Sweden Faculty of Computing Universiti Teknologi MalaysiaSkudai 81310Malaysia

The state space explosion,a challenge analogous to that encountered in a Petri net(PN),has constrained the extensivestudy of fuzzy Petri nets(FPNs).Current reasoning algorithms employing FPNs,which operate through forward,backward,andbidirectional mechanisms,are *** algorithms streamline the inference process by eliminating irrelevant componentsof the ***,as the scale of the FPN grows,the complexity of these algorithms escalates sharply,posing a significantchallenge for practical *** address the state explosion issue,this work introduces a parallel bidirectional reasoningalgorithm for an FPN that utilizes reverse and decomposition strategies to optimize the implementation *** algorithminvolves hierarchically dividing a large-scale FPN into two sub-FPNs,followed by a converse operation to generate the reversalsub-FPN for the *** detailed mapping between the original and reversed FPNs is thoroughly *** operations are then conducted on the left-sub-FPN and the resulting reversal right-sub-FPN,with the final resultderived by computing the Euclidean distance between the outcomes from the output places of the two sub-FPNs.A case study ispresented to illustrate the implementation process,demonstrating the algorithm’s significant enhancement of inference efficiencyand substantial reduction in execution time.

关键词： Fuzzy Petri net(FPN) State explosion Decomposition Parallel Bidirectional reasoning

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Benchmarking domain-specific pretrained language models to identify the best model for methodological rigor in clinical studies

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Journal of Biomedical Informatics 2025年 166卷 104825页

作者： Zhou, Fangwen Parrish, Rick Afzal, Muhammad Saha, Ashirbani Haynes, R. Brian Iorio, Alfonso Lokker, Cynthia Health Information Research Unit Department of Health Research Methods Evidence and Impact Faculty of Health Sciences McMaster University HamiltonON Canada Department of Computing Faculty of Computing Engineering and the Built Environment Birmingham City University Birmingham United Kingdom Department of Oncology Faculty of Health Sciences McMaster University HamiltonON Canada Department of Medicine Faculty of Health Sciences McMaster University HamiltonON Canada

Objective: Encoder-only transformer-based language models have shown promise in automating critical appraisal of clinical literature. However, a comprehensive evaluation of the models for classifying the methodological rigor of randomized controlled trials is necessary to identify the more robust ones. This study benchmarks several state-of-the-art transformer-based language models using a diverse set of performance metrics. Methods: Seven transformer-based language models were fine-tuned on the title and abstract of 42,575 articles from 2003 to 2023 in McMaster University's Premium LiteratUre Service database under different configurations. The studies reported in the articles addressed questions related to treatment, prevention, or quality improvement for which randomized controlled trials are the gold standard with defined criteria for rigorous methods. Models were evaluated on the validation set using 12 schemes and metrics, including optimization for cross-entropy loss, Brier score, AUROC, average precision, sensitivity, specificity, and accuracy, among others. Threshold tuning was performed to optimize threshold-dependent metrics. Models that achieved the best performance in one or more schemes on the validation set were further tested in hold-out and external datasets. Results: A total of 210 models were fine-tuned. Six models achieved top performance in one or more evaluation schemes. Three BioLinkBERT models outperformed others on 8 of the 12 schemes. BioBERT, BiomedBERT, and SciBERT were best on 1, 1 and 2 schemes, respectively. While model performance remained robust on the hold-out test set, it declined in external datasets. Class weight adjustments improved performance in most instances. Conclusion: BioLinkBERT generally outperformed the other models. Using comprehensive evaluation metrics and threshold tuning optimizes model selection for real-world applications. Future work should assess generalizability to other datasets, explore alternate imbalance

关键词： Deep learning

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The Design Innovation Space of Intelligent Personal Assistants (IPAs) in Healthy Buildings in the Era of Artificial Intelligence 26th

The Design Innovation Space of Intelligent Personal Assistan...

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26th International Conference on Human-Computer Interaction, HCII 2024

作者： Liu, Yuqi Zhang, Jiawen Zeng, Yibei Liu, Yunlu Shidujamam, Mohammad Academy of Arts and Design Tsinghua University Beijing China Department of Service Design School of Design Royal College of Art London United Kingdom Fine Arts College Shanghai Normal University Shanghai China Faculty of Science Engineering and Computing Kingston University London United Kingdom Department of Computer Science and Engineering Independent University Dhaka Bangladesh

ISBN: (纸本)9783031768088

The implementation of Intelligent Personal Assistants (IPA) in healthy buildings cannot just elevate the buildings, intelligence level, but also boost their interactivity and interaction, enhance the quality of life for residents, and establish tailored health management approaches. Given China’s status as the world’s most populous country and a significant consumer market for emerging smart personal assistant products, this study focuses on the Chinese market. Utilizing a sample of 665 user data, it defines the demand, function, and design domains of IPA products in healthy buildings. Furthermore, it establishes a diversified design innovation space model for IPAs in healthy buildings amidst the artificial intelligence era. Initially, the study quantitatively assesses the impact of demographic variables on the acceptance of IPAs, offering a preliminary clarification and definition of the user profile of Chinese consumers. Additionally, by analyzing user needs and preferences, it explores the specific factors that guide users, purchasing decisions and categorizes these factors into four levels based on their significance. In terms of functional domains, this study has comprehensively gathered and categorized 30 service functions pertinent to Intelligent Personal Assistants (IPA), encompassing both current and anticipated functionalities. Utilizing questionnaire responses and statistical analysis, the study ranked the significance and precedence of these service functions, while employing principal component analysis to categorize user needs for smart speakers into seven distinct dimensions. Additionally, focusing on design domains, this research summarizes the diverse and innovative spatial models for intelligent personal assistants within architectural spaces, drawing insights from the functional domain analysis. This comprehensive study serves as a crucial foundation for the innovative design of IPA products in healthy buildings, while also facilitating the expans

关键词： Architectural design

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“Lost in the crowd”: ethical concerns in crowdsourced evaluations of LLMs

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AI and Ethics 2025年 1-7页

作者： Cheong, Marc Bush, Gabby Wildenauer, Michael Centre for AI and Digital Ethics University of Melbourne Melbourne Connect Parkville Australia School of Computing and Information Systems Faculty of Engineering and IT University of Melbourne Melbourne Connect Parkville Australia

With the acceleration of Large Language Model (LLM) use by the public, there is an urgent need to make sure the downstream effects have beneficial impacts on humans and society. Therefore, there is an increasing push for ethical evaluation of LLMs to look for potential bias, toxic behaviour, and misinformation. Thus, crowdsourcing has become a popular practice, such as OpenAI’s 2023 Evals initiative. Firstly, by reviewing the literature from software development and ethics, we wish to highlight several cautions on applying the crowdsourcing model to LLMs: including participant self-selection and non-representativeness; the diffusion of responsibility effect including ethics washing and burden shifting; and requisition of ‘incentives’ vis-a-vis issues faced by gig workers. Using the Evals GitHub repository as a case study, we study the effectiveness of an expert-driven, voluntary, crowdsourced scheme on GitHub to address socioethical issues in LLMs. This is achieved by evaluating the statistics of crowdsourced contributions on ethical and bias considerations, which pales in comparison to other technical contributions. This commentary hopes to highlight the issues of ethics, equity, and justice in LLM crowdsourcing, drawing upon interdisciplinary literature, and presents open considerations on how we can improve the state of play.

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Numerical Investigation of the Effect of an Oblique Flow Entry on the Pressure Losses in Square Channels

arXiv

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arXiv 2025年

作者： Samuels, Callum Watling, Timothy C. Aleksandrova, Svetlana Medina, Humberto Holtzman, Ran Rusli, Ijhar Benjamin, Stephen Coventry University United Kingdom Johnson Matthey plc Reading United Kingdom School of Engineering University of Leicester United Kingdom Faculty of Engineering University of Nottingham United Kingdom Faculty of Engineering Environment & Computing Coventry University United Kingdom

Flows in square channels are common in applications, such as automotive after-treatment systems and heat exchangers. Flows with axial flow entry are well understood, but for oblique flow entry, there is no clarity on the additional pressure loss magnitude or the flow regime. Laminar flow is often assumed, even though flow separation at the channel entrance can cause a transition to turbulence. Here, the impact of oblique flow entry on the flow is investigated using LES (Large Eddy Simulation) and RANS (Reynolds Averaged Navier Stokes) models, and their advantages and limitations are identified. The LES simulations show that the shear layer at the channel entrance produces continuous shedding of eddies that persist downstream even at moderate channel Reynolds numbers (≈ 2000). The predicted pressure losses mostly agree with experimental data. The differences observed for some parameters are attributed to the difficulty of accurately replicating the experimental geometry. It is shown that LES results are susceptible to the rounding of the leading edge (present in experiments). Including edge rounding improves the pressure predictions. RANS simulations predicted pressure losses within 5% of experimental values for most cases, apart from where transitional flow was observed (resulting in differences up to 40%). This study provides insights into the flow structure and sources of pressure losses in square channels and highlights the importance of understanding key flow and geometric features when using LES to predict complex flows involving flow separation and shear layers. © 2025, CC BY.

关键词： Laminar flow

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