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4th IEEE International Conference on Electrical, computer, and Energy Technologies, ICECET 2024

作者： Diakhame, Mouhamadou Lamine Diallo, Cherif Mejri, Mohamed Gaston Berger University Department of Computer Science Saint Louis Senegal Laval University Department of Computer Science & Software Engineering Quebec Canada

ISBN: (纸本)9798350395914

The correlation of security alerts is crucial for effective threat detection and mitigation in modern cybersecurity landscapes. With the exponential growth in the volume of generated alerts, there arises an imperative to continually develop new, efficient methods for alert correlation. The use of Large Language Models (LLMs) presents new avenues for enhancing real-time threat detection in the field of cybersecurity. In this paper, we introduce MCM-Llama, a fine-tuned language model aimed at improving security event correlation for real-time threat detection. Building upon our prior research, we augment the current framework by integrating MCM-Llama and introducing a novel architecture to practically implement our proposition. In doing so, we replace the correlation module previously based on Named Entity Recognition (NER) and semantic similarity with a fine-tuned Large Language Model (LLM). This transition allows us to capitalize on the advanced capabilities of LLM in comprehending and correlating security events dynamically. Through this enhancement, we aim to significantly improve the accuracy and efficiency of our threat detection system while leveraging the full potential of LLM technology in cybersecurity. Our experimental results validate the efficacy of the proposed approach by significantly improving the correlation rate, thereby reinforcing the pivotal role of Large Language Models (LLMs) in advancing security event correlation and threat detection methodologies. © 2024 IEEE.

关键词： Semantics

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Enhancing Ropes for Collaborative Text Editing

Enhancing Ropes for Collaborative Text Editing

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2023 Congress in computer Science, computer engineering, and Applied Computing, CSCE 2023

作者： Sandal, Semih Ovatman, Tolga Aselsan Inc Software Engineering Department Ankara Turkey Istanbul Technical University Department of Computer Engineering Istanbul Turkey

ISBN: (纸本)9798350327595

Rope is a tree-based data structure providing performance enhancements in string concatenation operations in applications such as text editors. Even though collaborative text editing has become one of the major applications in cloud environments, utilizing ropes in collaborative text editing is rarely discussed in current literature. In this study, we enhance the Rope data structure for collaborative text editing. Firstly we discuss possible issues that Ropes may suffer in collaborative text editing and propose enhancements that adapt Ropes to such environments. During this adaptation we pay special attention to keep Ropes' immutable nature for the insert operations and balance the tree by following AVL-tree-style and Red-Black-Tree-style methods. We also provide a performance comparison of those balancing approaches. In our results, it can be seen that the AVL-Rope and Red-Black-Rope are slower in the concatenation operation due to balancing, but faster in index search operation and insert operation than the unbalanced Rope. © 2023 IEEE.

关键词： Balanced trees Collaborative text editing Rope data structure String processing

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Regularized forensic efficient net: a game theory based generalized approach for video deepfakes detection

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Multimedia Tools and Applications 2024年 1-44页

作者： Ain, Qurat Ul Javed, Ali Malik, Khalid Mahmood Irtaza, Aun Department of Computer Science University of Engineering and Technology-Taxila Punjab Taxila47050 Pakistan Department of Software Engineering University of Engineering and Technology-Taxila Punjab Taxila47050 Pakistan College of Innovation and Technology University of Michigan-Flint FlintMI48502 United States

Due to the advancements in cutting-edge generative AI algorithms, generating hyper realistic deepfake videos has become easier for the public. This hyperrealism consequently fails contemporary methods to reliably discriminate between original and fake videos. Therefore, to counter any threat caused by these next-generation artificially generated videos, dependable approaches are required to address this classification challenge. To achieve this objective this paper presents an interdisciplinary approach that integrates game theory with deep learning to bring a novel solution to the problem of deepfake detection and protect the detectors against anti-forensics attack. To the best of our knowledge, there does not exist any other work dedicated to video deepfake detection using the integrated approach of game theory and deep learning. The game is designed for two players to distinguish between pristine and deepfake videos. The game utilizes different strategies for the data manipulator as a player P1 and the deepfake detector as P2. Strategies used for P1 involve the formation of the subsets like open and close-set, combined subsets, imbalanced dataset, and post-processing attacks to create challenging strategies for P2. To counter the strategies of P1,we propose a novel Regularized Forensic Efficient Net (RFE Net) that employs regularization techniques, such as batch normalization, dropout, augmentation, and early stopping. Based on the P1 move, the detector chooses the regularization techniques by considering factors such as generalizability and efficiency. Regularization-based strategies improve the performance of our model when compared to contemporary methods. Computation of the Nash equilibrium with the proposed zero-sum game helps to effectively detect deepfakes and leads the game to maximum payoff. Performance of the proposed game theory-based RFE Net was measured on standard and diverse datasets of FaceForensic++, DFDC preview, CelebDF, DFFD, and the World lea

关键词： Nash equilibrium

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Parallel Iterative FEM Solver with Initial Guess for Frequency Domain Electromagnetic Analysis

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Intelligent Automation & Soft Computing 2023年第5期36卷 1585-1602页

作者： Woochan Lee Woobin Park Jaeyoung Park Young-Joon Kim Moonseong Kim Department of Electrical Engineering Incheon National UniversityIncheon22012Korea Department of Computer Engineering Hongik UniversitySeoul04066Korea Department of Electronic Engineering Gachon UniversitySeongnam13120Korea Department of IT Convergence Software Seoul Theological UniversityBucheon14754Korea

The finite element method is a key player in computational electromag-netics for designing RF(Radio Frequency)components such as *** frequency-domain analysis is fundamental to identify the characteristics of the *** the conventional frequency-domain electromagnetic analysis using FEM(Finite Element Method),the system matrix is complex-numbered as well as indeﬁ*** iterative solvers can be faster than the direct solver when the solver convergence is guaranteed and done in a few ***,such complex-numbered and indeﬁnite systems are hard to exploit the merit of the iterative *** is also hard to beneﬁt from matrix factorization techniques due to varying system matrix parts according to ***,it is hard to adopt conventional iterative solvers even though the system matrix is sparse.A new parallel iterative FEM solver for frequency domain analysis is implemented for inhomogeneous waveguide structures in this *** this implementation,the previous solution of the iterative solver of Matlab(Matrix Laboratory)employ-ing the preconditioner is used for the initial guess for the next step’s solution *** overlapped parallel stage using Matlab’s Parallel Computing Toolbox is also proposed to alleviate the cold starting,which ruins the convergence of early steps in each parallel *** experiments based on waveguide structures have demonstrated the accuracy and efﬁciency of the proposed scheme.

关键词： Computational electromagnetics numerical simulation ﬁnite element method parallel processing iterative solvers

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A Hybrid Deep Fused Learning Approach to Segregate Infectious Diseases

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computers, Materials & Continua 2023年第2期74卷 4239-4259页

作者： Jawad Rasheed Shtwai Alsubai Department of Software Engineering Nisantasi UniversityIstanbul34398Turkey Dapartment of Computer Science College of Computer Engineering and Sciences in Al-KharjPrince Sattam Bin Abdulaziz UniversityP.O.Box 151Al-Kharj11942Saudi Arabia

Humankind is facing another deadliest pandemic of all times in history,caused by *** from this challenging pandemic,World Health Organization(WHO)considers tuberculosis(TB)as a preeminent infectious disease due to its high infection ***,both TB and COVID-19 severely affect the lungs,thus hardening the job of medical practitioners who can often misidentify these diseases in the current ***,the time of need calls for an immediate and meticulous automatic diagnostic tool that can accurately discriminate both *** one of the preliminary smart health systems that examine three clinical states(COVID-19,TB,and normal cases),this study proposes an amalgam of image filtering,data-augmentation technique,transfer learning-based approach,and advanced deep-learning classifiers to effectively segregate these *** first employed a generative adversarial network(GAN)and Crimmins speckle removal filter on X-ray images to overcome the issue of limited data and *** pre-processed image is then converted into red,green,and blue(RGB)and Commission Internationale de l’Elcairage(CIE)color spaces from which deep fused features are formed by extracting relevant features using DenseNet121 and *** feature extractor extracts 1000 most useful features which are then fused and finally fed to two variants of recurrent neural network(RNN)classifiers for precise discrimination of threeclinical *** analysis showed that the proposed Bi-directional long-short-term-memory(Bi-LSTM)model dominated the long-short-termmemory(LSTM)network by attaining an overall accuracy of 98.22%for the three-class classification task,whereas LSTM hardly achieved 94.22%accuracy on the test dataset.

关键词： computer-aided diagnosis decision support system deep transfer learning deep fused features tuberculosis COVID-19

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AI-Driven Contextual Story Creation with Integrated Text and Visual Generation

AI-Driven Contextual Story Creation with Integrated Text and...

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2024 Innovations in Intelligent Systems and Applications Conference, ASYU 2024

作者： Gulsoy, Muhammed Kokach, Vael Kocaçınar, Büşra Akbulut, Fatma Patlar Department of Computer Engineering Istanbul Kültür University Istanbul Turkey Department of Software Engineering Istanbul Kültür University Istanbul Turkey

ISBN: (纸本)9798350379433

This study proposes an AI-driven framework for generating coherent narratives and corresponding visuals tailored for children's stories. We produce textual material by using Long Short-Term Memory (LSTM) models, and visually appealing images by using Generative Adversarial Networks (GANs) and fine-tuned Stable Diffusion Models. Furthermore, we investigate various prefix-based story starting points to increase the variety and interest of the story. Our method includes a significant innovation: the Stable Diffusion model is fine-tuned for image generation, enabling the production of high-quality, contextually relevant images from particular story segments. We show by experiments that the Stable Diffusion model can be fine-tuned to greatly improve the quality and relevance of the produced images, and that the layered LSTM model with extended training produces the best text generation performance. Using these advanced models together improved the storytelling experience by fusing imaginative AI with rich, narrative-driven imagery. © 2024 IEEE.

关键词： Generative adversarial networks

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UVM Based Verification Framework for Deep Learning Hardware Accelerator: Case Study 4

UVM Based Verification Framework for Deep Learning Hardware ...

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4th IEEE International Conference on Electrical, computer, and Energy Technologies, ICECET 2024

作者： Aboudeif, Randa Awaad, Tasneem A. Abdelsalam, Mohamed Ismail, Yehea The American University in Cairo Siemens Digital Industries Software Electronics and Communications Engineering Department Cairo Egypt Ain Shams University Siemens Digital Industries Software Department of Computer and Systems Engineering Faculty of Engineering Cairo Egypt Siemens Digital Industries Software Cairo Egypt The American University in Cairo Electronics and Communications Engineering Department Cairo Egypt

ISBN: (纸本)9798350395914

Hardware Verification of Deep Learning Accelerators (DLAs) has become critically important for testing the reliability and trustworthiness of Learning Enabled Autonomous Systems (LEAS). In this paper, we introduce a scalable, reusable, and efficient hardware verification framework for DLAs using the Universal verification methodology (UVM). The verification environment is focused on testing the inference functionality of the perception module in LEAS for different DLA designs. Moreover, the verification environment is portable across simulation and hardware-assisted verification platforms for emulation and FPGA prototyping. To assess our proposed UVM design methodology, we have applied it to the Nvidia Deep Learning Accelerator (NVDLA), an open-source core for DLAs as a case study. © 2024 IEEE.

关键词： CNN Deep Learning Accelerators NVDLA UVM veri-fication

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Alzheimer Disease Detection Studies: Perspective on Multi-Modal Data

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Yearbook of Medical Informatics 2025年第1期33卷 266-276页

作者： Dehghani, Farzaneh Derafshi, Reihaneh Lin, Joanna Bayat, Sayeh Bento, Mariana Biomedical Engineering Department University of Calgary Canada Computer Science Department University of Calgary Canada Geomatics Engineering Department University of Calgary Canada Hotchkiss Brain Institute Cumming School of Medicine University of Calgary Canada Electrical and Software Engineering Department University of Calgary Canada

OBJECTIVES: Alzheimer's Disease (AD) is one of the most common neurodegenerative diseases, resulting in progressive cognitive decline, and so accurate and timely AD diagnosis is of critical importance. To this end, various medical technologies and computer-aided diagnosis (CAD), ranging from biosensors and raw signals to medical imaging, have been used to provide information about the state of AD. In this survey, we aim to provide a review on CAD systems for automated AD detection, focusing on different data types: namely, signals and sensors, medical imaging, and electronic medical records (EMR). METHODS: We explored the literature on automated AD detection from 2022-2023. Specifically, we focused on various data resources and reviewed several preprocessing and learning methodologies applied to each data type, as well as evaluation metrics for model performance evaluation. Further, we focused on challenges, future perspectives, and recommendations regarding automated AD diagnosis. RESULTS: Compared to other modalities, medical imaging was the most common data type. The prominent modality was Magnetic Resonance Imaging (MRI). In contrast, studies based on EMR data type were marginal because EMR is mostly used for AD prediction rather than detection. Several challenges were identified: data scarcity and bias, imbalanced datasets, missing information, anonymization, lack of standardization, and explainability. CONCLUSION: Despite recent developments in automated AD detection, improving the trustworthiness and performance of these models, and combining different data types will improve usability and reliability of CAD tools for early AD detection in the clinical practice. The Author(s). This is an open access article published by Thieme under the terms of the Creative Commons Attribution License, permitting unrestricted use, distribution, and reproduction so long as the original work is properly cited. (https://***/licenses/by/4.0/).

关键词： Alzheimer’s Disease computer-aided Diagnosis Electronic Medical Records Medical Imaging Signals and Sensors

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Understanding the Impact of Unexpected Cobot Movements on Human Stress Levels: A Time Series Classification Task

Understanding the Impact of Unexpected Cobot Movements on Hu...

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2024 Workshop on Embracing Human-Aware AI in Industry 5.0, HAII5.0 2024

作者： Shilon, Lior Shevtsov, Vladyslav Schillreff, Nadia Rother, Anne Mark, Shlomo Spiliopoulou, Myra Department of Software Engineering Sami Shamoon College of Engineering Ashdod Israel Faculty of Computer Science Otto-von-Guericke-University Magdeburg Germany

This study investigates the impact of unexpected stimuli on participants’ stress levels during human-robot interactions (HRI). We designed an experiment, where a cobot performs writing tasks, as well as some unexpected actions, intended to surprise the human observer. During this experiment, we are continuously monitoring physiological responses through Electrodermal Activity (EDA) and Electrocardiogram (ECG) sensors. Our goal was to capture the relationship between surprising actions by the cobot and indicators of change in the human stress levels. We employed time series classification techniques, namely ROCKET, Shapelet Transform and WEASEL, along with Support Vector Machine (SVM) analysis on temporal segments. This approach enabled us to develop a binary classification model on stress levels. Our research contributes to the design of AI systems that can detect changes in the stress levels of humans who interact with cobots that perform unexpected actions. Thus, we work towards the improvement of safety protocols in HRI. © 2024 Copyright for this paper by its authors.

关键词： Collaborative robots

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A Novel Color Image Watermarking Method with Adaptive Scaling Factor Using Similarity-Based Edge Region

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computer Systems Science & engineering 2023年第10期47卷 55-77页

作者： Kali Gurkahraman Rukiye Karakis Hidayet Takci Faculty of Engineering Department of Computer EngineeringSivas Cumhuriyet UniversitySivas58140Turkey Faculty of Technology Department of Software EngineeringSivas Cumhuriyet UniversitySivas58140Turkey

This study aimed to deal with three challenges:robustness,imperceptibility,and capacity in the image watermarking *** reach a high capacity,a novel similarity-based edge detection algorithm was developed that finds more edge points than traditional *** colored watermark image was created by inserting a randomly generated message on the edge points detected by this *** ensure robustness and imperceptibility,watermark and cover images were combined in the high-frequency subbands using Discrete Wavelet Transform and Singular Value *** the watermarking stage,the watermark image was weighted by the adaptive scaling factor calculated by the standard deviation of the similarity *** to the results,the proposed edge-based color image watermarking technique has achieved high payload capacity,imperceptibility,and robustness to all *** addition,the highest performance values were obtained against rotation attack,to which sufficient robustness has not been reached in the related studies.

关键词： Image watermarking edge detection discrete wavelet transform singular value decomposition adaptive scaling factor

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