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2nd International Conference on Cyber Resilience, ICCR 2024

作者： Safi, Wassim Ghwanmeh, Sameh Mahfuri, Mahmoud Al-Sit, Waleed T. Higher Institute For Applied Sciences And Technology-HIAST Damascus Syria American University In The Emirates Department Of Computer Science United Arab Emirates International University For Science And Technology Computer And Information Technology Department Damascus Syria College Of Engineering Mu'tah University Department Of Computer Engineering Al-karak Jordan Higher Colleges Of Technology Dubai United Arab Emirates

ISBN: (纸本)9798350394962

Cloud Computing (CC) has ushered in a paradigm shift in the provisioning of IT resources, offering users enhanced cost-efficiency and streamlined infrastructure management. However, this growth in CC adoption has also brought forth a myriad of security challenges, encompassing threats to availability, integrity, and confidentiality. To combat these challenges, Cloud Service Providers (CSPs) are increasingly turning to Machine Learning (ML) to diminish the necessity for human intervention in the identification and resolution of security issues. ML's capacity to process extensive datasets and make highly accurate predictions holds the potential to revolutionize CSPs' approach to security. This paper delves into recent advancements in the realm of ML-based security within Cloud Computing. It assesses the attributes and efficacy of various ML algorithms, underscoring their distinctive strengths and possible limitations. Our objective is to present a comprehensive overview of the current landscape of ML in cloud security, illuminating the promising prospects this emerging field holds. © 2024 IEEE.

关键词： Cloud security

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Real-time Light Estimation and Neural Soft Shadows for AR Indoor Scenarios

arXiv

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arXiv 2023年

作者： Sommer, Alexander Schwanecke, Ulrich Schoemer, Elmar Computer Vision and Mixed Reality Group RheinMain University of Applied Sciences Wiesbaden Rüsselsheim Germany Institute of Computer Science Johannes Gutenberg University Mainz Germany

We present a pipeline for realistic embedding of virtual objects into footage of indoor scenes with focus on real-time AR applications. Our pipeline consists of two main components: A light estimator and a neural soft shadow texture generator. Our light estimation is based on deep neural nets and determines the main light direction, light color, ambient color and an opacity parameter for the shadow texture. Our neural soft shadow method encodes object-based realistic soft shadows as light direction dependent textures in a small MLP. We show that our pipeline can be used to integrate objects into AR scenes in a new level of realism in real-time. Our models are small enough to run on current mobile devices. We achieve runtimes of 9ms for light estimation and 5ms for neural shadows on an iPhone 11 Pro. © 2023, CC BY-NC-SA.

关键词： Augmented reality

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MANA Cryptocurrency: Transactional Dynamics & Financial Metrics Study

MANA Cryptocurrency: Transactional Dynamics & Financial Metr...

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2024 International Conference on Smart Devices, ICSD 2024

作者： Kumar, Amit Sharma, Neha Chauhan, Rahul Joshi, Kireet Raj, Ankush Iqbal, Mohammed Ismail Chitkara University Institute of Engineering & Technology Chitkara University Punjab Rajpura India Graphic Era Hill University Computer Science & Engineering Dehradun India Graphic Era Deemed to be University Computer Science & Engineering Uttarakhand Dehradun248002 India University of Jammu Department of Computer Science and IT India University of Technology and Applied Sciences Petroleum Engineering Department Nizwa Oman

ISBN: (纸本)9798350394412

This research investigates the emerging domain of the Metaverse, a digital realm that is significantly influencing social interactions and economic landscapes. This study aims to investigate the complex variables that contribute to the economic valuation of virtual properties on the Decentraland platform, which is a famous Metaverse platform. Despite the growing significance of the Metaverse and its status as a prominent economic force, there is a noticeable lack of empirical research examining the distinct dynamics of land valuation within this context. The current corpus of scholarly work mostly focuses on valuations in real-world contexts, resulting in a significant knowledge gap about the specific factors that influence land prices in the Metaverse. This research seeks to address the existing vacuum in knowledge by doing a comprehensive literature review and platform analysis. The primary objective is to provide insights into the various elements that influence the financial prospects of Metaverse lands. The anticipated results of this study are expected to provide significant contributions to the comprehension of Metaverse economics. Additionally, these findings will serve as a foundation for future research endeavors that aim to explore the convergence of virtual and physical realms in relation to financial value. © 2024 IEEE.

关键词： Decentralized finance

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MODULAR ROOM SIMULATION FOR THE IVES 3D ENGINE 10

MODULAR ROOM SIMULATION FOR THE IVES 3D ENGINE

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10th Convention of the European Acoustics Association, EAA 2023

作者： Dziwis, Damian Lübeck, Tim Pörschmann, Christoph Institute of Computer and Communication Technology TH Köln - University of Applied Sciences Cologne Germany Audio Communication Group Technische Universität Berlin Germany

ISBN: (纸本)9788888942674

IVES (Interactive Virtual Environment System) is a modular open-source 3D engine for creating virtual worlds in the Max programming environment. It is intended both for artists to create 3D audiovisual compositions and for researchers to use as a basis for prototyping new algorithms or experimental setups. IVES is based on Max's OpenGL implementation Jitter and IRCAM's spatial audio library Spat. This paper presents the latest development, a room simulation for IVES's parametric rendering. It consists of Spat's Feedback-Delay Network (FDN) for late reflections, accompanied by an Image-Source Model (ISM) module for early reflections and a generator for designing rooms. Here, the geometric parameters of the desired rooms are determined, and material properties are assigned to reflective surfaces. This results in parameters for reflective surfaces, filters, and attenuation for the ISM module and frequency band-specific RT60 times for the reverberation module using the FDN. The mirror image sources generated by the ISM are fed into the subsequent parametric rendering chain as additional sound sources, and late reverberation from the FDN reverberation module is added to the encoded sound field as spherical harmonic coefficients. We describe the underlying concept and technical implementation, and the possibilities resulting from the open and modular implementation. © 2023 Damian Dziwis et al. This is an open-access article distributed under the terms of the Creative Commons Attribution 3.0 Unported License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

关键词： Reverberation

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An Overview of Moving Object Detection Using YOLO Deep Learning Models 2

An Overview of Moving Object Detection Using YOLO Deep Learn...

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2nd International Conference on Disruptive Technologies, ICDT 2024

作者： Dwivedi, Upendra Joshi, Kireet Shukla, Surendra Kumar Rajawat, Anand Singh G L Bajaj Institute of Technology and Management Department of Applied Computational Science and Engineering Greater Noida India Graphic Era Deemed to Be University Department of Computer Science and Engineering Dehradun India SVKM S Nmims Mpstme Department of Computer Science and Engineering Shirpur Campus Shirpur India School of Computer Sciences and Engineering Sandip University Nashik India

ISBN: (纸本)9798350371055

computer vision is a promising domain that focuses on emerging approaches, algorithms and technologies to provide computing capability to machine to analysis visual data, such as image files, videos files and real time video streaming. In computer vision and image processing detecting object from images and videos has been topic of extensive research. This is accomplished by applying various computer vision techniques to analyze the visual data and determine class of objects from image and videos files. One widespread approach to object detection is using the deep learning models. YOLO (You Only Look Once) is a convolutional neural network that offers a fast and efficient solution to object detection using deep learning. It enable computer to handle real-time detection of objects in video frames and accurately locate and classify moving objects. YOLO is able to simultaneously detect and classify objects in an efficient manner using convolutional neural networks. © 2024 IEEE.

关键词： Deep Learning Image Processing Motion Detection Moving Object Detection YOLO

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Interactive High-Resolution Simulation of Granular Material

arXiv

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arXiv 2023年

We introduce a particle-based simulation method for granular material in interactive frame rates. We divide the simulation into two decoupled steps. In the first step, a relatively small number of particles is accurately simulated with a constraint-based method. Here, all collisions and the resulting friction between the particles are taken into account. In the second step, the small number of particles is significantly increased by an efficient sampling algorithm without creating additional artifacts. The method is particularly robust and allows relatively large time steps, which makes it well suited for real-time applications. With our method, up to 500k particles can be computed in interactive frame rates on consumer CPUs without relying on GPU support for massive parallel computing. This makes it well suited for applications where a lot of GPU power is already needed for render tasks. © 2023, CC BY-NC-SA.

关键词： Program processors

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Use of Polygram Substitution in Spotting the Swindler in Secret Sharing 11th

Use of Polygram Substitution in Spotting the Swindler in Sec...

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11th International Conference on Intelligent Computing and Applications, ICRTC 2023

作者： Yadav, Poonam Satheesh Kumar, S. Muthukumaran, V. Sandeep Kumar, Mathivanan Radhika, E.K. Department of Computer Science School of Applied Sciences Reva University Bangalore India Department of Mathematics College of Engineering and Technology SRM Institute of Science and Technology Tamil Nadu Kattankulathur 603203 India School of Computer Science and Engineering Galgotias University Uttar Pradesh Greater Noida203201 India Department of Computer Science and Applications Sindhi College Bangalore560024 India

ISBN: (纸本)9789819717231

In the polygram substitution, one set of characters is replaced by another set based on algorithm. The model is proposed for calculating and estimating secure online secret shares, where dynamic modification in any of the shares will be identified if number of the shares are more than specified threshold value. The security is based on the intractability of factoring as it uses Lagrange polynomial function. This is further enhanced by a graphical masking and unmasking technique;it includes two phases: encryption of the individual secrets and sharing of the data. The generation of the shares is done by masking using Polygram substitution, and the shares are regenerated and unmasking of the qualified shares is done to find original secret. Masking is done on original secret, then shares are generated. Each encrypted share contains information which is different than original secret. Masking by Polygram substitution gives additional protection of secret data on the Internet. Moreover, we achieve efficient recovery of the original secret data. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Polynomials

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Anticancer Drug Response Prediction Using Deep Learning

Anticancer Drug Response Prediction Using Deep Learning

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Information Technology and Engineering (HITE), Horizons of

作者： Awais Raza Zaidi Abdul Majid Muhammad Bilal Department of Computer & Information Sciences Pakistan Institute of Engineering & Applied Sciences Islamabad Islamabad Pakistan

ISBN: (数字)9798331516055

ISBN: (纸本)9798331516062

Precision medicine demands accurate prediction of individual patient responses to drugs, a challenge addressed by various computational methods leveraging genomic profiles or chemical structures. This study introduces a novel deep-learning model designed to amalgamate genetic mutation, gene expression, and cheminformatics data within a multi-task convolutional architecture. we identified pertinent cancer-related genes from oncology genetics databases and L1000 landmark genes. The model, incorporating gene expression and mutations as genomic features, and Chemoinformatics features from PubChem or ChEMBL, exhibited enhanced predictive performance through the synergistic integration of these diverse data types. Our approach extends beyond conventional Graph Neural Networks (GNNs) by replacing them with Recurrent Neural Networks (RNNs) in the variants autoencoder deep-learning network. Additionally, we augmented the model's performance by increasing the number of layers in the Convolutional Neural Network (CNN). The extended architecture, complemented by multi-tasking and self-attention functions, demonstrated superior predictive capabilities compared to recently published methods on the same datasets. Our deep learning model has demonstrated improved performance as compared to the previous models. The results underscore the potential of combining gene expression, genetic mutation, and Chemoinformatics features to enhance the accuracy and effectiveness of predictive modeling in the context of precision medicine.

关键词： Drugs Accuracy Precision medicine Genomics Predictive models Multitasking Data models Gene expression Biological cells Cancer

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Maize EfficientNet Fusion: Advancing Maize Disease Detection with MF-NET 25

Maize EfficientNet Fusion: Advancing Maize Disease Detection...

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25th International Conference on Digital Image Computing: Techniques and Applications, DICTA 2024

作者： Khalid, Fatima Hanif, Muhammad Ul Ain, Qurat Ghulam Ishaq Khan Institute of Engineering Sciences and Engineering Faculty of Computer Science and Engineering KPK Topi Pakistan Ghulam Ishaq Khan Institute of Engineering Sciences and Engineering Aerial Robotics and Vision Lab KPK Topi Pakistan Shifa Tameer-E-Millat University Department of Computing Islamabad Pakistan

ISBN: (纸本)9798350379037

Maize is a vital global crop, essential for food security but highly susceptible to diseases that threaten yield and quality. Traditional methods for detecting these diseases are computationally intensive and rely on high-quality images, limiting their practical use in diverse field conditions. This research addresses these challenges by proposing MF-Net, a novel model that leverages two EfficientNet-b0 architectures with ReLU and Swish activation functions. Our approach involves partially training Model R with frozen early layers using ReLU, and fully training Model S with Swish activation for enhanced optimization and robustness. We employ model concatenation and feature fusion techniques to create a lightweight yet powerful model, complemented by additional layers for improved feature extraction and regularization. Experimental results on the Corn Disease and Severity (CD&S) dataset are compelling: MF-Net achieves a remarkable 95.7% accuracy and 96% precision in disease classification, and 88.3% accuracy and 88.46% precision in severity level detection. These results highlight the model's effectiveness under challenging conditions such as cluttered backgrounds and variable lighting, significantly reducing computation time without compromising accuracy. Hence, MF-Net presents an innovative and efficient solution for maize disease detection and severity assessment, offering significant advancements for agricultural practices and crop management. © 2024 IEEE.

关键词： CD&S Dataset EffiecientNet-b0 Fused Truncated EfficientNet Maize Disease Maize Severity Level

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Research on Biomedical Engineering 2025年第2期41卷 1-19页

作者： A, Padmashree Sankar, P. Alkhayyat, Ahmad Muniyandy, Elangovan Department of Computer Science and Business Systems Bannari Amman Institute of Technology Tamil Nadu Sathyamangalam India Department of Computer Science and Engineering Saveetha Engineering College Tamil Nadu Chennai India Department of Computers Techniques Engineering College of Technical Engineering The Islamic University Najaf Iraq Department of Computers Techniques Engineering College of Technical Engineering The Islamic University of Al Diwaniyah Al Diwaniyah Iraq Department of Biosciences Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Tamil Nadu Chennai India Applied Science Research Center Applied Science Private University Amman Jordan

Purpose: The primary objective of this research is to develop a comprehensive framework for the analysis of brain tumor images, addressing the complexities of detection, segmentation, and classification. Given the intricate nature of brain anatomy and tumor variability, the study aims to enhance the accuracy and efficiency of brain tumor diagnosis using advanced imaging and processing techniques. Methods: The framework utilizes Brain Tumor Segmentation (BraTS) 2019 and 2020 datasets, applying an AMW-PM filter to enhance image quality. Tumor segmentation employs a slimmable transformer with hybrid axial attention for precision. Classification uses similarity navigated graph neural networks (SNGNNs), optimized with the crayfish algorithm (CFO) to minimize loss and enhance performance. Results: The framework’s effectiveness was validated using key performance metrics, showing significant improvements in tumor detection and classification. The combination of advanced segmentation techniques and the SNGNN classifier, optimized by the CFO, led to a fivefold increase in diagnostic accuracy. These improvements demonstrated a substantial enhancement in the model's ability to correctly identify tumor types and provide reliable outputs for clinical use. Conclusion: The proposed framework delivers a robust solution for brain tumor analysis, integrating cutting-edge techniques for image processing, segmentation, and classification. The results not only highlight improvements in diagnostic accuracy but also contribute to better clinical decision-making in managing brain tumors. This framework sets the foundation for future advancements in brain tumor diagnostics. © The Author(s), under exclusive licence to The Brazilian Society of Biomedical Engineering 2025.

关键词： Graph neural networks

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