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Model of fractured medium and nondestructive control of composite materials

Chinese Journal of Aeronautics 2024年第2期37卷 93-99页

作者： Vasily GOLUBEV Ilia NIKITIN Katerina BEKLEMYSHEVA Department of Computers Science and Numerical Mathematics Moscow Institute of Physics and TechnologyDolgoprudny 141700Russia Mathematical Modeling and Management Department of InformatizationInstitute of Computer Aided Design RASMoscow 123056Russia

Non-destructive testing of composites is an important issue in the modern aircraft *** are susceptible to the barely visible impact damage which can affect the residual strength of the material and occurs both during production and *** continuum model for describing the damaged zone is *** slip theory relations used for a continuous distribution of slip planes are *** the initial stage,the isotropic background model is *** model allows the material slippage along the fractures based on the Coulomb friction law with the small viscous *** this regime,the govern system of equations becomes *** overcome this difficulty,the explicit-implicit grid-characteristic scheme is *** standard ultrasound diagnostic procedure of damaged composite materials is successfully *** with the trivial free-surface fracture model,different reactions on the compression and stretch waves are *** approach provided an effective way for the simulation of complex dynamic behavior of damage zones.

关键词： Non-destructive testing Composite materials Numerical modeling Continuum model Grid-characteristic method

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StealthFace:Transfer Learning-Based Ensemble Model for Disguised Face Recognition using Skin-Segmented Images

IAENG International Journal of Computer Science

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IAENG International Journal of computer science 2025年第4期52卷 1244-1260页

作者： Padmashree, G. Kotegar, Karunakar A Department of Data Science and Computer Applications Manipal Institute of Technology Manipal Academy of Higher Education Karnataka Manipal576104 India

Disguised face identification is challenging since people cover their identities by wearing masks, hats, sunglasses, or other disguises. These disguises dramatically modify face features, making identifying individuals a difficult task. In this study, we introduce StealthFace, an ensemble model that utilizes transfer learning and skin-segmented images to improve the recognition of disguised faces. To improve the accuracy and resilience of disguised face recognition, StealthFace leverages the power of deep learning algorithms, transfer learning, and ensemble methods. The framework employs pre-trained convolutional neural network models, such as ResNet50 and DenseNet121 for feature extraction and to learn discriminative features that are important in identifying individuals despite the disguises. The ensemble approach combines models’ predictions, utilizing their collective expertise and capturing multiple perspectives on disguised faces. Our ensemble model outperforms other state-of-the-art methods with an overall accuracy of 99.24% and contributes to enhancing security measures and advancing the development of face recognition systems. © (2025), (International Association of Engineers). All Rights Reserved.

关键词： Contrastive Learning

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ETiSeg-Net: edge-aware self attention to enhance tissue segmentation in histopathological images

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Multimedia Tools and Applications 2024年 1-21页

作者： Rashmi, R. Girisha, S. Department of Computer Science and Engineering Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Manipal India Department of Data Science and Computer Applications Manipal Institute of Technology Manipal Academy of Higher Education Manipal India

Digital pathology employing Whole Slide Images (WSIs) plays a pivotal role in cancer detection. Nevertheless, the manual examination of WSIs for the identification of various tissue regions presents formidable challenges due to its labor-intensive nature and subjective interpretation. Convolutional Neural Network (CNN) based semantic segmentation algorithms have emerged as valuable tools for assisting in this task by automating ROI delineation. The incorporation of attention modules and carefully designed loss functions has shown promise in further augmenting the performance of these algorithms. However, there exists a notable gap in research regarding the utilization of attention modules specifically for tissue segmentation, thereby constraining our comprehension and application of these modules in this context. This study introduces ETiSeg-Net (Edge-aware self attention to enhance Tissue Segmentation), a CNN-based semantic segmentation model that uses a novel edge-based attention module to achieve effective delineation of class boundaries. In addition, an innovative iterative training strategy is devised to efficiently optimize the model parameters. The study also conducts a comprehensive investigation into the impact of attention modules and loss functions on the efficacy of semantic segmentation models. Qualitative and quantitative evaluations of these semantic segmentation models are conducted using publicly available datasets. The findings underscore the potential of attention modules in enhancing the accuracy and effectiveness of tissue semantic segmentation. © The Author(s) 2024.

关键词： Semantic Segmentation

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REAL-TIME GLUCOSE MONITORING SYSTEM AND DIETARY RECOMMENDATIONS FOR DIABETES MELLITUS USING FOG COMPUTING

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Telecommunications and Radio Engineering (English translation of Elektrosvyaz and Radiotekhnika) 2025年第5期84卷 55-67页

作者： Sathish, N. Elangovan, D. Nagalakshmi, R. Suresh, G. Chitra, Devi D. Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India Department of Information Technology Saveetha Engineering College Chennai India Department of Computer Science and Engineering SRM Institute of Science and Technology Ramapuram Chennai India Department of Artificial Intelligence and Data Science Kings Engineering College Sriperumbudur India Department of Computer Science And Engineering S.A. Engineering College Chennai India

This systematic review gave special attention to diabetes and the advancements in food and nutrition needed to prevent or manage diabetes in all its forms. There are two main forms of diabetes mellitus: Type 1 (T1D) and Type 2 (T2D). The loss of beta cells brought on by an autoimmune reaction causes the pancreas to be unable to produce enough insulin, which leads to Type 1 diabetes. A deficiency of insulin could arise as the illness worsens. The most frequent cause is a combination of excessive body weight and inadequate exercise. Currently, 90–95% of patients have Type 2 diabetes mellitus (T2DM). Age-specific normal blood sugar ranges exist and knowing them is crucial for determining a target blood sugar level that is both healthy and appropriate. The main takeaway is that diet regimens ought to be tailored to the patient’s requirements and should account for their capacity for change. Carbohydrates are the nutrients that have the biggest impact on the rise in blood glucose. In this paper, we have proposed a novel method for glucose monitoring and diet recommendations for diabetic patients using fog computing. A paradigm known as fog computing makes use of the benefits that come from using both cloud and edge devices to support modern computing systems by offering high-quality services, cutting latency, facilitating mobility, supporting multi-tenancy, and many other features. © 2025 by Begell House, Inc.

关键词： Fog computing

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A Novel Eccentric Intrusion Detection Model Based on Recurrent Neural Networks with Leveraging LSTM

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computers, Materials & Continua 2024年第3期78卷 3089-3127页

作者： Navaneetha Krishnan Muthunambu Senthil Prabakaran Balasubramanian Prabhu Kavin Kishore Senthil Siruvangur Kavitha Chinnadurai Jehad Ali Department of Computer Applications Karpagam College of EngineeringCoimbatore641032India Department of Computer Science and Engineering Karpagam College of EngineeringCoimbatore641032India Department of Data Science and Business Systems College of Engineering and TechnologySRM Institute of Science and TechnologyKattankulathur603203India Department of Computer Science and Engineering Sathyabama Institute of Science and TechnologyJeppiaar NagarRajiv Gandhi SalaiChennai600119India Department of AI Convergence Network Ajou UniversitySuwon16499Korea

The extensive utilization of the Internet in everyday life can be attributed to the substantial accessibility of online services and the growing significance of the data transmitted via the ***,this development has expanded the potential targets that hackers might *** adequate safeguards,data transmitted on the internet is significantly more susceptible to unauthorized access,theft,or *** identification of unauthorised access attempts is a critical component of cybersecurity as it aids in the detection and prevention of malicious *** research paper introduces a novel intrusion detection framework that utilizes Recurrent Neural Networks(RNN)integrated with Long Short-Term Memory(LSTM)*** proposed model can identify various types of cyberattacks,including conventional and distinctive *** networks,a specific kind of feedforward neural networks,possess an intrinsic memory *** Neural Networks(RNNs)incorporating Long Short-Term Memory(LSTM)mechanisms have demonstrated greater capabilities in retaining and utilizing data dependencies over extended *** such as data types,training duration,accuracy,number of false positives,and number of false negatives are among the parameters employed to assess the effectiveness of these models in identifying both common and unusual *** are utilised in conjunction with LSTM to support human analysts in identifying possible intrusion events,hence enhancing their decision-making capabilities.A potential solution to address the limitations of Shallow learning is the introduction of the Eccentric Intrusion Detection *** model utilises Recurrent Neural Networks,specifically exploiting LSTM *** proposed model achieves detection accuracy(99.5%),generalisation(99%),and false-positive rate(0.72%),the parameters findings reveal that it is superior to state-of-the-art techniques.

关键词： Cybersecurity intrusion detection machine learning leveraging long short-term memory(LLSTM) CICIDS2019 dataset innovative cyberattacks

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Erdős–Pósa property of obstructions to interval graphs

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Journal of Graph Theory 2023年第4期102卷 702-727页

作者： Agrawal, Akanksha Lokshtanov, Daniel Misra, Pranabendu Saurabh, Saket Zehavi, Meirav Department of Computer Science Ben-Gurion University of the Negev Beersheba Israel Department of Computer Science University of California Santa BarbaraCA United States Department of Computer Science Max Planck Institute for Informatics Saarbrucken Germany Department of Computer Science University of Bergen Bergen Norway Department of Computer Science The Institute of Mathematical Sciences HBNI Chennai India

A class of graphs (Formula presented.) admits the Erdős–Pósa property if for any graph (Formula presented.), either (Formula presented.) has (Formula presented.) vertex-disjoint "copies" of the graphs in (Formula presented.), or there is a set (Formula presented.) of (Formula presented.) vertices that intersects all copies of the graphs in (Formula presented.). For any graph class (Formula presented.), it is natural to ask whether the family of obstructions to (Formula presented.) has the Erdős–Pósa property. In this paper, we prove that the family of obstructions to interval graphs—namely, the family of chordless cycles and asteroidal witnesses (AWs)—admits the Erdős–Pósa property. In turn, this yields an algorithm to decide whether a given graph (Formula presented.) has (Formula presented.) vertex-disjoint AWs and chordless cycles, or there exists a set of (Formula presented.) vertices in (Formula presented.) that hits all AWs and chordless cycles. © 2022 Wiley Periodicals LLC.

关键词： Graphic methods

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Spatio-Spectral Graph Neural Networks 38

Spatio-Spectral Graph Neural Networks

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Geisler, Simon Kosmala, Arthur Herbst, Daniel Günnemann, Stephan Department of Computer Science Munich Data Science Institute Technical University of Munich Germany

Spatial Message Passing Graph Neural Networks (MPGNNs) are widely used for learning on graph-structured data. However, key limitations of -step MPGNNs are that their "receptive field" is typically limited to the -hop neighborhood of a node and that information exchange between distant nodes is limited by oversquashing. Motivated by these limitations, we propose Spatio-Spectral Graph Neural Networks (S2GNNs) - a new modeling paradigm for Graph Neural Networks (GNNs) that synergistically combines spatially and spectrally parametrized graph filters. Parameterizing filters partially in the frequency domain enables global yet efficient information propagation. We show that S2GNNs vanquish over-squashing and yield strictly tighter approximation-theoretic error bounds than MPGNNs. Further, rethinking graph convolutions at a fundamental level unlocks new design spaces. For example, S2GNNs allow for free positional encodings that make them strictly more expressive than the 1-Weisfeiler-Leman (WL) test. Moreover, to obtain general-purpose S2GNNs, we propose spectrally parametrized filters for directed graphs. S2GNNs outperform spatial MPGNNs, graph transformers, and graph rewirings, e.g., on the peptide long-range benchmark tasks, and are competitive with state-of-the-art sequence modeling. On a 40 GB GPU, S2GNNs scale to millions of nodes. © 2024 Neural information processing systems foundation. All rights reserved.

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LLM-based methods for the creation of unit tests in game development 28th

LLM-based methods for the creation of unit tests in game dev...

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28th International Conference on Knowledge Based and Intelligent information and Engineering Systems, KES 2024

作者： Paduraru, Ciprian Staicu, Adelina Stefanescu, Alin University of Bucharest Department of Computer Science Romania Institute of Logic and Data Science Romania

Problems related to the quality of games, whether on the initial release or after updates, can lead to player dissatisfaction, media attention, and potential financial setbacks. These issues can stem from software bugs, performance bottlenecks, or security vulnerabilities. Despite these challenges, game developers often rely on manual playtesting, highlighting the need for more robust and automated processes in game development. This research explores the application of Large Language Models (LLMs) to automate the creation of unit tests in game development, focusing on strongly typed programming languages such as C++ and C#, which are widely used in the industry. The study focuses on fine-tuning Code Llama, an advanced code generation model, to address common scenarios in game development, including game engines and specific APIs or backends. Although the prototyping and evaluations primarily took place within the Unity game engine, the proposed methods can be adapted to other internal or publicly available solutions. The evaluation results demonstrate these methods' effectiveness in improving existing unit test suites or automatically generating new tests based on natural language descriptions of class contexts and targeted methods. © 2024 The Authors.

关键词： C++ (programming language)

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Exploring Neural Network Structure through Sparse Recurrent Neural Networks: A Recasting and Distillation of Neural Network Hyperparameters 22

Exploring Neural Network Structure through Sparse Recurrent ...

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22nd IEEE International Conference on Machine Learning and Applications, ICMLA 2023

作者： Hershey, Quincy Paffenroth, Randy Pathak, Harsh Data Science Worcester Polytechnic Institute Worcester United States Mathematical Sciences Computer Science and Data Science Worcester Polytechnic Institute Worcester United States

ISBN: (纸本)9798350345346

This paper explores the role of sparse parameteri-zations on Recurrent Neural Network (RNN) performance using anomaly detection tasks. The findings indicate sparsity plays a significant role in both improving training stability and overall performance of RNNs. Sparsity of the weight matrix is initially left to vary while the dimensions of the weight matrix remain fixed, causing total trainable weights to vary in response. The sparse RNN models demonstrate surprisingly low performance sensitivity to the resulting changes in total trainable weights. Building on these results, sparsity of the weight matrix is again left to vary with a fixed number of trainable weights and the dimensions of the weight matrix permitted to change with sparsity. By isolating the impact of sparse parameterizations, RNNs show improved training stability and overall performance. Moreover, the results indicate an optimal band of sparsity within which RNNs perform most efficiently across a difficult time-series task. Sparse RNN configurations are compared to LSTM models in anomaly detection tasks. Sparse RNN models demonstrate a strong performance advantage over LSTM models, even out-performing in scenarios where memory is crucial and LSTM models are expected to perform better. The results indicate the reputation of RNN training instability may be exacerbated by overparamaterization resulting from dense configurations. Several characteristics such as model depth and model sparsity are presented which significantly improve the stability and expressiveness of model performance across a wider array of hyperparameters while improving performance differentials at significantly reduced weight counts. Ultimately the paper finds the potential of RNNs to be better realized through sparse parameterizations. © 2023 IEEE.

关键词： recurrent neural networks RNN sparsity

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Sequentia12D: Organizing Center of Skip Connections for Transformers 22

Sequentia12D: Organizing Center of Skip Connections for Tran...

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22nd IEEE International Conference on Machine Learning and Applications, ICMLA 2023

作者： Pathak, Harsh Nilesh Paffenroth, Randy Hershey, Quincy Data Science Worcester Polytechnic Institute Worcester United States Mathematical Sciences Computer Science and Data Science Worcester Polytechnic Institute Worcester United States

ISBN: (纸本)9798350345346

Skip connections (SCs) are commonly employed in neural networks to facilitate gradient-based training and often lead to improved performance in deep learning. To implement SCs, a user writes custom modules along with *** which limits the extensibility of neural network design with SC. Despite their versatility and user-friendliness, there is an opportunity to create a standardized approach for organizing SCs in various applications. Establishing a clear organizing framework would significantly enhance the widespread adoption of this powerful technique, amplifying its impact. We introduce Sequential2D as an enhanced orchestrator which provides in-sights into not only the width and depth of the network but also a neural network's intra-connections such as SCs. The functionality provided by the Sequential2D architecture is a strict super-set of the functionality found in the common *** network. The 2D in Sequential2D refers to the two-dimensional matrix of functions, as opposed to the one-dimensional cascading array of functions in a Pytorch or Keras sequential container. As an example of our proposed approach, we demonstrate the effectiveness of our Sequential2D architecture by substantially improving the fine-tuning performance of the GPT-2 model with less than 1% additional parameters. © 2023 IEEE.

关键词： deep learning GPT-2 Neural Network nlp recurrent neural networks RNN Sequential skip-connection sparsity transformer

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