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25th International Arab Conference on Information Technology, ACIT 2024

作者： Arabiat, Mohammad Abuowaida, Suhaila Alshdaifat, Nawaf Aburomman, Ahmad Al Henawi, Esraa Dmour, Mohmmad Elrashidi, Ali Zarqa University Faculty of Information Technology Department of Computer Science Zarqa Jordan Al al-Bayt University Faculty of Information Technology Department of Computer Science Mafraq Jordan Applied Science Private University Faculty of Information Technology Amman Jordan Zarqa University Faculty of Information Technology Department of Data Science and Ai Zarqa Jordan University of Business and Technology Department of Electrical Engineering Jeddah21432 Saudi Arabia

ISBN: (纸本)9798331540012

In the last few years, artificial intelligence has shown its importance and ability in computer vision due to its high level of accuracy and wide acceptance. The main goal of the research is to look into a better way to separate different types of fruits. Although deep learning is used, the standard classification methods for different types of fruit don't work very well. The main steps of the recommended better multiple fruit classification method are: 1) Making changes to the filter's size. Selecting the most suitable number of repeats is part of the process of improving the ResNet-101 block. 3) It is proposed that there be one more convolutional layer. Exists support for the suggested method after collecting and labeling 27,000 pictures as part of an actual study. The dataset with many fruits is split into two parts: 80% is used for training and 20% is used for testing. These pictures were used to train an application based on deep learning (DL) to help successfully determine multiple fruit objects in images. Then and then possible to compare the results of this method to the most current methods in the area of object division. The application being studied at does better than expected in terms of accuracy AP in a range of levels. The values always go farther than the equivalent measures of three widely used methods. Compared to the other methods, the suggested one takes less time to train. There are higher numbers for the thresholds than there are for the state of the art method's thresholds © 2024 IEEE.

关键词： Deep reinforcement learning

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Generative Adversarial Networks for Spatio-Spectral Compression of Hyperspectral Images 14

Generative Adversarial Networks for Spatio-Spectral Compress...

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14th Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing, WHISPERS 2024

作者： Fuchs, Martin Hermann Paul Byju, Akshara Preethy Walda, Alisa Rasti, Behnood Demir, Begum Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Amritapuri India Technische Universität Berlin Faculty of Electrical Engineering and Computer Science Germany BIFOLD - Berlin Institute for the Foundations of Learning and Data Germany

ISBN: (纸本)9798331513139

Deep learning-based hyperspectral image (HSI) compression has recently attracted great attention in remote sensing due to the growth of hyperspectral data archives. Most of the existing models achieve either spectral or spatial compression and do not jointly consider the spatio-spectral redundancies present in HSIs. To address this problem, in this paper, we propose High Fidelity Compression (HiFiC)-based models for spatio-spectral compression of HSIs. In detail, we introduce two new models: i) HiFiC using Squeeze and Excitation (SE) blocks (denoted as HiFiCsE);and ii) HiFiC with 3D convolutions (denoted as HiFiC3D) in the framework of compression of HSIs. We analyze the effectiveness of HiFiCsE and HiFiC3D in compressing the spatio-spectral redundancies with channel attention and inter-dependency analysis. Experimental results show the efficacy of the proposed models in performing spatio-spectral compression, while reconstructing images at reduced bitrates with higher reconstruction quality. The code of the proposed models is publicly available at https://***/rsim/HSI-SSC. © 2024 IEEE.

关键词： Generative adversarial networks

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A Novel Hybrid Approach-Based on Heartbeat and Accelerometer Signals for Enhanced Security in WBSNs

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IEEE Sensors Journal 2025年第6期25卷 10295-10308页

作者： Yan, Guixiang Zhang, Guanghe Liu, Fanghua Williams Samuel, Oluwarotimi Aborokbah, Majed Guo, Jiquan Zhu, Liqing Wei, Sikang Jiangxi Normal University School of Computer and Information Engineering Nanchang330022 China University of Derby School of Computing and Data Science Research Centre DerbyDE22 3AW United Kingdom INTI International University Faculty of Data Science and Information Technology Nilai71800 Malaysia University of Tabuk Faculty of Computers and Information Technology Tabuk71491 Saudi Arabia Jiangxi Normal University School of Digital Industry Shangrao334000 China

In wireless body sensor networks (WBSNs), ensuring secure and efficient key distribution is critical, particularly given the limited computational and energy resources of the sensors. Existing methods often struggle to balance security with these resource constraints, especially in environments involving physiological data such as acceleration (ACC) and electrocardiogram (ECG) signals. For the first time, this study proposes a novel hybrid approach that integrates fuzzy commitment with ACC signal noise and ECG features for efficient key generation and distribution in WBSNs. By employing a low-pass filter to process ACC signal noise, we generated highly random binary sequences (BSs), leveraging the inherent randomness of the signal for secure key generation. Concurrently, an optimized coding scheme was built for ECG feature construction to ensure secure key distribution between devices. Extensive experiments, including entropy analysis and National Institute of Standards and Technology (NIST) statistical tests, confirm the robustness and security of our method. The proposed scheme achieves a false acceptance rate (FAR) of 5.23%, demonstrating superior performance across multiple databases in comparison to benchmark approaches. This novel dual-key generation strategy that combines the unpredictability of ACC noise with the individual-specific traits of ECG signals can significantly enhance the security, applicability, and versatility of WBSNs. © 2001-2012 IEEE.

关键词： Acceptance tests

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Tailor-Designed Models for the Turbulent Velocity Gradient through Normalizing Flow

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Physical Review Letters 2024年第18期133卷 184001页

作者： M. Carbone V. J. Peterhans A. S. Ecker M. Wilczek Theoretical Physics I Göttingen Germany Faculty of Physics Institute of Computer Science and Campus Institute Data Science

Small-scale turbulence can be comprehensively described in terms of velocity gradients, which makes them an appealing starting point for low-dimensional modeling. Typical models consist of stochastic equations based on closures for nonlocal pressure and viscous contributions. The fidelity of the resulting models depends on the accuracy of the underlying modeling assumptions. Here, we discuss an alternative data-driven approach leveraging machine learning to derive a velocity gradient model which captures its statistics by construction. We use a normalizing flow to learn the velocity gradient probability density function (PDF) from direct numerical simulation (DNS) of incompressible turbulence. Then, by using the equation for the single-time PDF of the velocity gradient, we construct a deterministic, yet chaotic, dynamical system featuring the learned steady-state PDF by design. Finally, utilizing gauge terms for the velocity gradient single-time statistics, we optimize the time correlations as obtained from our model against the DNS data. As a result, the model time realizations resemble the time series from DNS statistically closely.

关键词： Isotropic turbulence Turbulence Direct numerical simulations Machine learning Navier-Stokes equation

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Multi-trial Vector-based Whale Optimization Algorithm

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Journal of Bionic Engineering 2024年第3期21卷 1465-1495页

作者： Mohammad H.Nadimi-Shahraki Hajar Farhanginasab Shokooh Taghian Ali Safaa Sadiq Seyedali Mirjalili Faculty of Computer Engineering Najafabad BranchIslamic Azad UniversityNajafabad***Iran Big Data Research Center Najafabad BranchIslamic Azad UniversityNajafabad***Iran Department of Computer Science Nottingham Trent UniversityNottinghamNG118NSUK Centre for Artificial Intelligence Research and Optimisation Torrens UniversityBrisbane4006Australia

The Whale Optimization Algorithm(WOA)is a swarm intelligence metaheuristic inspired by the bubble-net hunting tactic of humpback *** spite of its popularity due to simplicity,ease of implementation,and a limited number of parameters,WOA’s search strategy can adversely affect the convergence and equilibrium between exploration and exploitation in complex *** address this limitation,we propose a new algorithm called Multi-trial Vector-based Whale Optimization Algorithm(MTV-WOA)that incorporates a Balancing Strategy-based Trial-vector Producer(BS_TVP),a Local Strategy-based Trial-vector Producer(LS_TVP),and a Global Strategy-based Trial-vector Producer(GS_TVP)to address real-world optimization problems of varied degrees of ***-WOA has the potential to enhance exploitation and exploration,reduce the probability of being stranded in local optima,and preserve the equilibrium between exploration and *** the purpose of evaluating the proposed algorithm's performance,it is compared to eight metaheuristic algorithms utilizing CEC 2018 test ***,MTV-WOA is compared with well-stablished,recent,and WOA variant *** experimental results demonstrate that MTV-WOA surpasses comparative algorithms in terms of the accuracy of the solutions and convergence ***,we conducted the Friedman test to assess the gained results statistically and observed that MTV-WOA significantly outperforms comparative ***,we solved five engineering design problems to demonstrate the practicality of *** results indicate that the proposed MTV-WOA can efficiently address the complexities of engineering challenges and provide superior solutions that are superior to those of other algorithms.

关键词： Swarm intelligence algorithms Metaheuristic algorithms Optimization Engineering design problems Whale optimization algorithm

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Deliberative XAI: How Explanations Impact Understanding and Decision-Making of AI Novices in Collective and Individual Settings

arXiv

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arXiv 2024年

作者： Schmude, Timothée Koesten, Laura Möller, Torsten Tschiatschek, Sebastian University of Vienna Faculty of Computer Science Research Network Data Science Doctoral School Computer Science Austria University of Vienna Faculty of Computer Science Research Group Visualization and Data Analysis Austria University of Vienna Faculty of Computer Science Research Group Visualization and Data Analysis Research Network Data Science Austria University of Vienna Faculty of Computer Science Research Network Data Science Research Group Data Mining and Machine Learning Austria

XAI research often focuses on settings where people learn about and assess algorithmic systems individually. However, as more public AI systems are deployed, it becomes essential for XAI to facilitate collective understanding and deliberation. We conducted a task-based interview study involving 8 focus groups and 12 individual interviews to explore how explanations can support AI novices in understanding and forming opinions about AI systems. Participants received a collection of explanations organized into four information categories to solve tasks and decide about a system’s deployment. These explanations improved or calibrated participants’ self-reported understanding and decision confidence and facilitated group discussions. Participants valued both technical and contextual information and the self-directed and modular explanation structure. Our contributions include an explanation approach that facilitates both individual and collaborative interaction and explanation design recommendations, including active and controllable exploration, different levels of information detail and breadth, and adaptations to the needs of decision subjects. Copyright © 2024, The Authors. All rights reserved.

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Private Language Models via Truncated Laplacian Mechanism

Private Language Models via Truncated Laplacian Mechanism

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2024 Conference on Empirical Methods in Natural Language Processing, EMNLP 2024

作者： Huang, Tianhao Yang, Tao Habernal, Ivan Hu, Lijie Wang, Di Lab Saudi Arabia King Abdullah University of Science and Technology Saudi Arabia Nankai University China Research Center Trustworthy Data Science and Security of the University Alliance Ruhr Faculty of Computer Science Ruhr University Bochum Germany

ISBN: (纸本)9798891761643

Deep learning models for NLP tasks are prone to variants of privacy attacks. To prevent privacy leakage, researchers have investigated word-level perturbations, relying on the formal guarantees of differential privacy (DP) in the embedding space. However, many existing approaches either achieve unsatisfactory performance in the high privacy regime when using the Laplacian or Gaussian mechanism, or resort to weaker relaxations of DP that are inferior to the canonical DP in terms of privacy strength. This raises the question of whether a new method for private word embedding can be designed to overcome these limitations. In this paper, we propose a novel private embedding method called the high dimensional truncated Laplacian mechanism. Specifically, we introduce a non-trivial extension of the truncated Laplacian mechanism, which was previously only investigated in one-dimensional space cases. Theoretically, we show that our method has a lower variance compared to the previous private word embedding methods. To further validate its effectiveness, we conduct comprehensive experiments on private embedding and downstream tasks using three datasets. Remarkably, even in the high privacy regime, our approach only incurs a slight decrease in utility compared to the non-private scenario. © 2024 Association for Computational Linguistics.

关键词： Differential privacy

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Brain Connectivity Using Deep Learning Algorithms 1

Brain Connectivity Using Deep Learning Algorithms

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2023 1st DMIHER International Conference on Artificial Intelligence in Education and Industry 4.0, IDICAIEI 2023

作者： Jiet, Moses Makuei Kamble, Aahash Puri, Chetan Gajanan Verma, Prateek Faculty of Engineering and Technology Department of Computer Science & Design Maharashtra Wardha442001 India Faculty of Engineering and Technology Department of Artificial Intelligence & Data Science Maharashtra Wardha442001 India

ISBN: (纸本)9798350338423

The integration of deep learning into the examination of brain connectivity marks a transformative era in understanding neural networks. This article provides a concise overview of the burgeoning field of deep learning in brain connectivity analysis. It defines the concept, explores functional and structural connectivity, outlines practical applications, delves into the algorithms involved, compares traditional methods vs advanced deep learning approaches for brain connectivity and highlights its revolutionary impact in clinical practice. In deep learning, brain connectivity investigates neural links using advanced artificial intelligence. Functional connectivity, powered by deep neural networks, reveals patterns in brain activity linked to cognition, emotion, and decision-making. Structural connectivity, often utilizing graph neural networks, reconstructs intricate neural pathways. This technology shows significant progress in diagnosing conditions like Alzheimer's, schizophrenia, and autism. As it evolves, its clinical importance becomes evident, offering early disease detection, tailored treatments, and a deeper understanding of neurological disorders. Beyond clinical applications, deep learning in brain connectivity opens avenues for advancements in brain-computer interfaces, enhanced rehabilitation therapies, and precise interventions. © 2023 IEEE.

关键词： Learning algorithms

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Enhancing Micro-Expression Recognition with Broadbent Attention Mechanism: A High-Performance Approach for Emotion Detection 21

Enhancing Micro-Expression Recognition with Broadbent Attent...

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21st IEEE International Conference on Smart Communities: Improving Quality of Life using AI, Robotics and IoT, HONET 2024

作者： Ashraf, Imran Kehkashan, Tanzila Zaman, Muhammad Akhunzada, Adnan Talib, Hafiz Khizer Bin Cao, Yanlong University of Lahore Faculty of Computer Science Lahore Pakistan Universiti Teknologi Malaysia Faculty of Computing Johor Bahru Malaysia COMSATS University Islamabad Department of Computer Science Islamabad Pakistan University of Doha for Science and Technology Department of Data and Cybersecurity Doha24449 Qatar School of Mechanical Engineering Zhejiang University Hangzhou China

ISBN: (纸本)9798350378078

Micro-expression recognition (MER) is a critical task in affective computing, yet it remains challenging due to the subtle, brief, and involuntary nature of micro-expressions. Existing methods often find it difficult to spot these quick micro facial expressions (MFEs), especially when there are obstacles, background noise, or differences between people. In this study, we propose a novel approach that integrates the Broadbent Attention Mechanism into a Convolutional Neural Network (CNN) to enhance MER. This attention mechanism is designed to focus on the most informative regions of facial images, improving the model's ability to extract relevant features and discard irrelevant information. The model is evaluated on the SAMM dataset, covering various emotions. Extensive experiments are conducted using both K-fold and Stratified K-fold cross-validation techniques to ensure a robust evaluation process. The proposed method achieves high accuracy 99.72%, precision 99.73%, Recall 99.72% and f1-Score 99.72% with Stratified K-fold & accuracy 99.65%, precision 99.65%, Recall 99.66% and f'1-Score 99.65% with K-fold, outperforming baseline models and demonstrating its effectiveness in recognizing subtle micro-expressions. These results indicate that the Broadbent Attention Mechanism significantly enhances accuracy and robustness of MER systems, offering a promising solution for applications in human-computer interaction (HCI) and emotion analysis. © 2024 IEEE.

关键词： Convolutional neural networks

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BAnDIT: Business Process Anomaly Detection in Transactions 29th

BAnDIT: Business Process Anomaly Detection in Transactions

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29th International Conference on Cooperative Information Systems, CoopIS 2023

作者： Rudolf, Nico Böhmer, Kristof Leitner, Maria Faculty of Computer Science Research Group Workflow Systems and Technology University of Vienna Währinger Straße 29 Vienna Austria Faculty of Informatics and Data Science Chair of Artificial Intelligence in IT Security University of Regensburg Regensburg Germany

ISBN: (纸本)9783031468452

Business process anomaly detection enables the prevention of misuse and failures. Existing approaches focus on detecting anomalies in control, temporal, and resource behavior of individual instances, neglecting the communication of multiple instances in choreographies. Consequently, anomaly detection capabilities are limited. This study presents a novel neural network-based approach to detect anomalies in distributed business processes. Unlike existing methods, our solution considers message data exchanged during process transactions. Allowing the generation of detection profiles incorporating the relationship between multiple instances, related services, and exchanged data to detect point and contextual anomalies during process runtime. To validate the proposed solution, it is demonstrated with a prototype implementation and validated with a use case from the ecommerce domain. Future work aims to further improve the deep learning approach, to enhance detection performance. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Anomaly detection

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