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arXiv 2025年

作者： Alwageed, Hathal S. Khan, Rafiq Ahmad College of Computer and Information Sciences Jouf University Sakaka42421 Saudi Arabia Software Engineering Research Group Department of Computer Science and IT University of Malakand Pakistan

As software security threats continue to evolve, the demand for innovative ways of securing coding has tremendously grown. The integration of Generative AI (GenAI) into software development holds significant potential for improving secure coding practices. This paper aims at systematically studying the impact of GenAI in enhancing secure coding practices from improving software security, setting forth its potential benefits, challenges, and implications. To outline the contribution of AI driven code generation tools, we analyze via a structured review of recent literature, application to the industry, and empirical studies on how these tools help to mitigate security risks, comply with the secure coding standards, and make software development efficient. We hope that our findings will benefit researchers, software engineers and cybersecurity professionals alike in integrating GenAI into a secure development workflow without losing the advantages GenAI provides. Finally, the state of the art advances and future directions of AI assisted in secure software engineering discussed in this study can contribute to the ongoing discourse on AI assisted in secure software engineering. © 2025, CC BY.

关键词： software design

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Influence of foam content on mechanical properties and thermal conductivity of ceramsite foam concrete

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Scientific Reports 2025年第1期15卷 1-19页

作者： Zhang, Jing Su, Yuxin Xiong, Yudong He, Jianming Xu, Qianyi Gao, Hongshuai School of Civil Engineering Heilongjiang University Harbin 150080 China College of Computer Science and Technology College of Software Heilongjiang University Harbin 150080 China Zhejiang Guangtian Component Group Co. Ltd. Ningbo 315000 China Heilongjiang North Resilient City Research Institute Harbin 150080 China

Ceramsite foam concrete (CFC) is an innovative lightweight building material formulated by integrating ceramsite into foam concrete. It retains the inherent advantages of foam concrete while mitigating high drying shrinkage and excessive water absorption, significantly enhancing durability and compressive strength. This study investigates the effects of foam content on CFC’s dry density, compressive strength, water absorption, softening coefficient, and thermal conductivity. The phenomena observed during the compression testing of the CFC specimens were also documented to analyze the degree of spalling. A novel predictive model for determining the thermal conductivity of CFC was originally developed and validated in this study. Results reveal strong linear relationships, foam content negatively correlates with density, strength, and softening coefficient, but positively correlates with thermal conductivity and water absorption. Increasing foam content also enlarges average pore size. These findings provide a theoretical foundation for optimizing CFC in energy-efficient building applications, offering significant potential for reducing energy consumption and improving thermal insulation in modern construction. © The Author(s) 2025.

关键词： Ceramsite Foam concrete Mechanical properties Thermal conductivity

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Towards a component infrastructure for Cyber-Physical Systems 16

Towards a component infrastructure for Cyber-Physical System...

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31st Annual ACM Symposium on Applied Computing, SAC 2016

作者： Maia, Marcio E. F. Andrade, Rossana M. C. Viana, Windson Group of Computer Networks Software Engineering and Systems Campus de Quixada Federal University of Ceara Brazil Group of Computer Networks Software Engineering and Systems Computer Science Department Federal University of Ceara Brazil Group of Computer Networks Software Engineering and Systems Instituto UFC Virtual Federal University of Ceara Brazil

ISBN: (纸本)9781450337397

The proximity between physical and cyber worlds is permitting the appearance of Cyber-Physical Systems (CPS), formed by users and devices (physical) interacting with services, components and applications (cyber). In CPS, where unpredictability is an important challenge, two characteristics must be present in the developed systems: i) low coupling among interacting modules and ii) self-adaptive capabilities. In that direction, this paper describes an uncoupled sensor and actuator component-based infrastructure for self adaptive CPS. Its main contribution is two-fold: i) a lightweight and interoperable mechanism to specify and manage sensors and actuators, built on top of an ii) uncoupled communication and coordination mechanism for cyberphysical entities. © 2016 ACM.

关键词： Cyber Physical System

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Left inferior parietal lobe and auditory cortex jointly contribute to sound knowledge retrieval

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Brain stimulation 2025年第4期18卷 1037-1047页

作者： Philipp Kuhnke Ole Numssen Johannah Voeller Vincent K M Cheung Konstantin Weise Markus Kiefer Gesa Hartwigsen Wilhelm Wundt Institute for Psychology Leipzig University Germany Research Group Cognition and Plasticity Max Planck Institute for Human Cognitive and Brain Sciences (MPI CBS) Leipzig Germany. Electronic address: philipp.kuhnke@uni-leipzig.de. Research Group Cognition and Plasticity Max Planck Institute for Human Cognitive and Brain Sciences (MPI CBS) Leipzig Germany Methods and Development Group Brain Networks MPI CBS Leipzig Germany TMS Group Department of Neuroscience and Biomedical Engineering Aalto University Finland TMS Lab Athinoula A. Martinos Center for Biomedical Imaging Massachusetts General Hospital Boston US. Department of Experimental Psychology University of Granada Spain Mind Brain and Behavior Research Center (CIMCYC) University of Granada Spain. Sony Computer Science Laboratories Tokyo Japan. Leipzig University of Applied Sciences HTWK Leipzig Germany. Department of Psychiatry Ulm University Germany. Research Group Cognition and Plasticity Max Planck Institute for Human Cognitive and Brain Sciences (MPI CBS) Leipzig Germany.

BACKGROUND:Conceptual knowledge is central to human cognition. Neuroimaging studies suggest that conceptual processing relies on the joint contribution of modality-specific perceptual-motor and multimodal brain regions. In particular, the multimodal left inferior parietal lobe (IPL) couples with auditory association cortex during sound knowledge retrieval and with somatomotor cortex during action feature retrieval. However, it remains unknown whether the interaction between modality-specific and multimodal cortices is causally relevant for conceptually-guided behavior. METHODS:To tackle this issue, we applied offline transcranial magnetic stimulation (TMS) over modality-specific cortex (somatomotor, auditory, or sham), before 24 healthy participants received online TMS over multimodal cortex (IPL, or sham) during action and sound knowledge tasks on written words. For the first time in a TMS study on higher cognition, TMS targeting and dosing were optimized using individual a priori computational electric field simulations. RESULTS:We found that combined stimulation of auditory association cortex and multimodal IPL selectively impaired sound judgments, but not action judgments, on low sound-low action words (BF = 5.316). Unifocal TMS over auditory cortex or IPL alone did not disrupt behavior, suggesting adaptive compensatory plasticity. CONCLUSIONS:These results indicate that the joint contribution of multimodal IPL and auditory cortex is causally relevant for sound knowledge retrieval: The functional relevance of left IPL depends on the integrity of the auditory cortex, and vice versa, as single perturbation of either region did not disrupt performance. Our findings support "hybrid theories" of conceptual processing, which propose that task-dependent functional interactions between modality-specific and multimodal regions are crucial.

关键词： TMS compensation computational modeling concepts plasticity semantics

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Automated engineered-stone silicosis screening and staging using Deep Learning with X-rays

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computers in Biology and Medicine 2025年 191卷 110153-110153页

作者： Priego-Torres, Blanca Sanchez-Morillo, Daniel Khalili, Ebrahim Conde-Sánchez, Miguel Ángel García-Gámez, Andrés León-Jiménez, Antonio Bioengineering Automation and Robotics Research Group Department of Automation Engineering Electronics and Computer Architecture and Networks School of Engineering University of Cadiz Cádiz Puerto Real11519 Spain Puerta del Mar University Hospital Cádiz11009 Spain Pulmonology Department Puerta del Mar University Hospital Cádiz11009 Spain Radiology Department Puerta del Mar University Hospital Cádiz11009 Spain Radiology Department Puerto Real University Hospital Cádiz Puerto Real11510 Spain

Silicosis, a debilitating occupational lung disease caused by inhaling crystalline silica, continues to be a significant global health issue, especially with the increasing use of engineered stone (ES) surfaces containing high silica content. Traditional diagnostic methods, dependent on radiological interpretation, have low sensitivity, especially, in the early stages of the disease, and present variability between evaluators. This study explores the efficacy of deep learning techniques in automating the screening and staging of silicosis using chest X-ray images. Utilizing a comprehensive dataset, obtained from the medical records of a cohort of workers exposed to artificial quartz conglomerates, we implemented a preprocessing stage for rib-cage segmentation, followed by classification using state-of-the-art deep learning models. The segmentation model exhibited high precision, ensuring accurate identification of thoracic structures. In the screening phase, our models achieved near-perfect accuracy, with ROC AUC values reaching 1.0, effectively distinguishing between healthy individuals and those with silicosis. The models demonstrated remarkable precision in the staging of the disease. Nevertheless, differentiating between simple silicosis and progressive massive fibrosis, the evolved and complicated form of the disease, presented certain difficulties, especially during the transitional period, when assessment can be significantly subjective. Notwithstanding these difficulties, the models achieved an accuracy of around 81% and ROC AUC scores nearing 0.93. This study highlights the potential of deep learning to generate clinical decision support tools to increase the accuracy and effectiveness in the diagnosis and staging of silicosis, whose early detection would allow the patient to be moved away from all sources of occupational exposure, therefore constituting a substantial advancement in occupational health diagnostics. © 2025 The Authors

关键词： Occupational diseases

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Feature-Oriented Nonfunctional Requirement Analysis for software Product Line

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Journal of computer Science & Technology 2009年第2期24卷 319-338页

作者：彭鑫 Seok-Won Lee 赵文耘 School of Computer Science Fudan University Knowledge-Intensive Software Engineering Research Group Department of Software and Information Systems College of Computing and Informatics University of North Carolina at Charlotte

Domain analysis in software product line （SPL） development provides a basis for core assets design and imple- mentation by a systematic and comprehensive commonality/variability analysis. In feature-oriented SPL methods, products of the domain analysis are domain feature models and corresponding feature decision models to facilitate application-oriented customization. As in requirement analysis for a single system, the domain analysis in the SPL development should con- sider both flmctional and nonfunctional domain requirements. However, the nonfunctional requirements （NFRs） are often neglected in the existing domain analysis methods. In this paper, we propose a context-based method of the NFR analysis for the SPL development. In the method, NFRs are materialized by connecting nonfunctional goals with real-world context, thus NFR elicitation and variability analysis can be performed by context analysis for the whole domain with the assistance of NFR templates and NFR graphs. After the variability analysis, our method integrates both functional and nonfunc- tional perspectives by incorporating the nonfunctional goals and operationalizations into an initial functional feature model. NFR-related constraints are also elicited and integrated. Finally, a decision model with both functional and nonfunctional perspectives is constructed to facilitate application-oriented feature model customization. A computer-aided grading system （CAGS） product line is employed to demonstrate the method throughout the paper.

关键词： software product line nonfunctional requirement domain analysis feature-oriented method variability analysis

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A SOM-based hierarchical model to short-term load forecasting

A SOM-based hierarchical model to short-term load forecastin...

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2005 IEEE Russia Power Tech, PowerTech

作者： Carpinteiro, Otávio A. S. Reis, Agnaldo J. R. Research Group on Computer Networks and Software Engineering Federal University of Itajubá 37500-903 Itajubá MG Brazil

ISBN: (纸本)9781424418749

This paper proposes a SOM-based hierarchical neural model to the problem of short-term load forecasting. The neural model is made up of two self-organizing map nets - one on top of the other. It has been successfully applied to domains which require time series analysis. The model was trained and assessed on load data extracted from a Brazilian electric utility. It was required to predict once every hour the electric load during the next 24 hours. The paper presents the results, and evaluates them.

关键词： Self organizing maps

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A neural model in intrusion detection systems

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16th International Conference on Artificial Neural networks, ICANN 2006

作者： Carpinteiro, Otávio A. S. Netto, Roberto S. Lima, Isaías Zambroni De Souza, Antonio C. Moreira, Edmilson M. Pinheiro, Carlos A. M. Research Group on Computer Networks and Software Engineering Federal University of Itajubá 37500-903 Itajubá MG Brazil

ISBN: (纸本)3540388710

The paper proposes the use of the multilayer perceptron model to the problem of detecting attack patterns in computer networks. The multilayer perceptron is trained and assessed on patterns extracted from the files of the Third International Knowledge Discovery and Data Mining Tools Competition. It is required to classify novel normal patterns and novel categories of attack patterns. The results are presented and evaluated in the paper. © Springer-Verlag Berlin Heidelberg 2006.

关键词： Multilayer neural networks

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A hierarchical hybrid neural model with time integrators in long-term peak-load forecasting

A hierarchical hybrid neural model with time integrators in ...

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International Joint Conference on Neural networks, IJCNN 2005

作者： Carpinteiro, Otávio A. S. Leme, Rafael C. Zambroni De Souza, Antonio C. Quintanilha Filho, Paulo S. Research Group on Computer Networks and Software Engineering Federal University of Itajubá 37500-903 Itajubá MG Brazil

ISBN: (纸本)0780390482

A novel hierarchical hybrid neural model to the problem of long-term peak-load forecasting is proposed in this paper. The neural model is made up of two self-organizing map nets - one on top of the other -, and a single-layer perceptron. It has application into domains in which the context information given by former events plays a primary role. The model is compared to a multilayer perceptron. Both the hierarchical and the multilayer perceptron models are trained and assessed on load data extracted from a North-American electric utility. They are required to predict either once every week or once every month the electric peak-load during the next two years. The results are presented and evaluated in the paper.

关键词： Neural networks

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A hybrid neural model in long-term electrical load forecasting

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16th International Conference on Artificial Neural networks, ICANN 2006

作者： Carpinteiro, Otaávio A. S. Lima, Isaiáas Leme, Rafael C. De Zambroni Souza, Antonio C. Moreira, Edmilson M. Pinheiro, Carlos A. M. Research Group on Computer Networks and Software Engineering Federal University of Itajubaá 37500-903 Itajubaá MG Brazil

ISBN: (纸本)3540388710

A novel hierarchical hybrid neural model to the problem of long-term electrical load forecasting is proposed in this paper. The neural model is made up of two self-organizing map nets - one on top of the other -, and a single-layer perceptron. It has application into domains which require time series analysis. The model is compared to a multilayer perceptron. Both the hierarchical and the multilayer perceptron models are endowed with time windows in their input layers. They are trained and assessed on load data extracted from a North-American electric utility. The models are required to predict once every week the electric peak-load and mean-load during the next two years. The results are presented and evaluated in the paper. © Springer-Verlag Berlin Heidelberg 2006.

关键词： Electric load forecasting

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