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2024 IEEE International Conference on Signal Processing, Informatics, Communication and Energy Systems, SPICES 2024

作者： Iyer, Ganesh Neelakanta Saengarunvong, Nattapong Le, Minh Hien School of Computing National University of Singapore Department of Computer Science Singapore Faculty of Engineering King Mongkut's University of Technology Department of Computer Engineering Thonburi Bangkok Thailand Case School of Engineering Case Western Reserve University Department of Computer and Data Sciences ClevelandOH United States

ISBN: (纸本)9798350376135

Kathakali, an ancient art form that dates back to the 17th century, is renowned for its intricate hand gestures, dance movements, musical accompaniment, elaborate costumes, and makeup. Mastering such an art form is a challenge even for the most dedicated enthusiasts, given its unconventional methods of expression. However, with current advanced technology, we can use this to aid the learning of unfamiliar subjects by classifying factors to help the new learners. In this paper, we focus on classifying one aspect of the play, which is the expressions on the actor's face given a picture using computer vision methods. To achieve the final model, we use a combination of Dual Shot Face Detector (DSFD) and Convolution Neural Network (CNN) and finally tuning up the epochs of the training process. With our dataset, the proposed model reached an accuracy of 93% among nine classes. This project hopes to introduce Kathakali to a wider audience and facilitate the learning process of this art form. © 2024 IEEE.

关键词： Convolutional neural networks

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Fast waveform generation for gravitational waves using evolutionary algorithms

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Physical Review D 2024年第4期110卷 043035页

作者： Quirijn Meijer Sarah Caudill Institute for Gravitational and Subatomic Physics (GRASP) Department of Physics Science Park 105 1098XG Amsterdam The Netherlands Department of Physics Center for Scientific Computing and Data Science Research

Gravitational-wave analyses depend heavily on waveforms that model the evolution of compact binary coalescences as seen by observing detectors. In many cases these waveforms are given by waveform approximants, models that approximate the amplitude and phase of the waveform at a set of frequencies. Because of their omnipresence, improving the speed at which approximants can generate waveforms is crucial to accelerating the overall analysis of gravitational-wave detections. An optimization algorithm is proposed that can select at which frequencies in the spectrum an approximant should compute the power of a waveform, and at which frequencies the power can be safely interpolated at a minor loss in accuracy. The algorithm used is an evolutionary algorithm modeled after the principle of natural selection, iterating frequency arrays that perform better at every iteration. As an application, the candidates proposed by the algorithm are used to reconstruct signal-to-noise ratios. It is shown that the imrphenomxphm approximant can be sped up by at least 30% at a loss of at most 2.87% on the drawn samples, measured by the accuracy of the reconstruction of signal-to-noise ratios. The behavior of the algorithm as well as lower bounds on both speedup and error are explored, leading to a proposed proof of concept candidate that obtains a speedup of 46% with a maximum error of 0.5% on a sample of the parameter space used.

关键词： General relativity Gravitational waves

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Contract-Inspired Contest Theory for Controllable Image Generation in Mobile Edge Metaverse

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IEEE Transactions on Mobile computing 2025年

作者： Liu, Guangyuan Du, Hongyang Wang, Jiacheng Niyato, Dusit Kim, Dong In Nanyang Technological University College of Computing and Data Science Energy Research Institute @ NTU Interdisciplinary Graduate Program Singapore University of Hong Kong Department of Electrical and Electronic Engineering Hong Kong Hong Kong Nanyang Technological University College of Computing and Data Science Singapore Sungkyunkwan University Department of Electrical and Computer Engineering Korea Republic of

The rapid advancement of immersive technologies has propelled the development of the Metaverse, where the convergence of virtual and physical realities necessitates the generation of high-quality, photorealistic images to enhance user experience. However, generating these images, especially through Generative Diffusion Models (GDMs), in mobile edge computing environments presents significant challenges due to the limited computing resources of edge devices and the dynamic nature of wireless networks. This paper proposes a novel framework that integrates contract-inspired contest theory, Deep Reinforcement Learning (DRL), and GDMs to optimize image generation in these resource-constrained environments. The framework addresses the critical challenges of resource allocation and semantic data transmission quality by incentivizing edge devices to efficiently transmit high-quality semantic data, which is essential for creating realistic and immersive images. The use of contest and contract theory ensures that edge devices are motivated to allocate resources effectively, while DRL dynamically adjusts to network conditions, optimizing the overall image generation process. Experimental results demonstrate that the proposed approach not only improves the quality of generated images but also achieves superior convergence speed and stability compared to traditional methods. This makes the framework particularly effective for optimizing complex resource allocation tasks in mobile edge Metaverse applications, offering enhanced performance and efficiency in creating immersive virtual environments. © 2002-2012 IEEE.

关键词： Resource allocation

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Deep Learning Image-Based Plant Disease Classification - A Review

Deep Learning Image-Based Plant Disease Classification - A R...

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2024 International Conference on Computer and Applications, ICCA 2024

作者： Tartoussi, Israa Dornaika, Fadi Alirezazadeh, Pendar El Jaam, Jihad University of the Basque Country UPV/EHU Department of Computer Science and Artificial Intelligence San Sebastian Spain University of the Basque Country UPV/EHU Ikerbasque Basque Foundation for Science San Sebastian Spain School of Computing and Data Liverpool J. Mores University Liverpool United Kingdom

ISBN: (纸本)9798350367560

Effective classification of plant diseases is crucial for increasing agricultural productivity and ensuring global food se-curity. Deep learning, in particular convolutional neural networks (CNN s), has proven to be a revolutionary approach for plant disease classification using image data. This review examines prominent deep learning works that have been successfully applied to plant disease classification. The strengths of these works are highlighted, particularly their ability to accurately identify specific visual markers of disease, enabling precise differentiation in a wide range of plants. By utilizing extensive agricultural datasets, the deep models consistently achieve high accuracy and outperform traditional manual methods in terms of speed and reliability. However, challenges such as the variability of datasets, environmental influences on image quality and the computational cost of training and deploying CNN models are also addressed. This review highlights the potential of deep learning-based plant disease classification as a scalable and efficient solution for precision agriculture, enabling early detection and intervention to mitigate crop losses and improve agricultural sustainability. © 2024 IEEE.

关键词： Convolutional neural networks

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Joint Optimization of Model Splitting and Device Task Assignment for Private Multi-hop Split Learning 59

Joint Optimization of Model Splitting and Device Task Assign...

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59th Annual Conference on Information sciences and Systems, CISS 2025

作者： Wei, Dongyu Zhang, Qianqian Liu, Yuchen Chen, Mingzhe Rowan University Department of Electrical and Computer Engineering GlassboroNJ08028 United States Nc State University Department of Computer Science RaleighNC27695 United States University of Miami Department of Electrical and Computer Engineering Coral GablesFL33146 United States University of Miami Frost Institute for Data Science and Computing Coral GablesFL33146 United States

ISBN: (纸本)9798331513269

In this paper, a deceptive signal-assisted private split learning is investigated. In our model, several edge devices jointly perform collaborative training, and some eavesdroppers aim to collect the model and data information from devices. To prevent the eavesdropper from collecting model and data information, a subset of devices can transmit deceptive signals. Therefore, it is necessary to determine the subset of devices used for deceptive signal transmission, the subset of model training devices, and the models assigned to each model training device. This problem is formulated as an optimization problem whose goal is to minimize the eavesdropped information while meeting the model training energy consumption and delay constraints. To solve this problem, we propose an actor-critic deep reinforcement learning (AC-DRL) framework that enables a centralized agent to dynamically determine the model training devices, the deceptive signal transmission devices, the transmit power, and sub-models assigned to each model training device while considering network topology and device properties (e.g., energy and privacy constraints). The proposed AC-DRL framework integrates an actor network to generate optimal actions and a critic network to stabilize the learning process by evaluating the cumulative rewards. Simulation results demonstrate that the proposed method improves the convergence rate by 3× and the accumulated reward by up to 40% compared with the proximal policy optimization (PPO) algorithm. © 2025 IEEE.

关键词： Deep reinforcement learning

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Enhancing Cloud Image Retrieval Efficiency through Secure Optimization

Enhancing Cloud Image Retrieval Efficiency through Secure Op...

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2024 International Conference on Knowledge Engineering and Communication Systems, ICKECS 2024

作者： Balakrishna, N. Sakthivel, M. Ushasri, G.Sai Thanmai, J.Naga Kumar, K.Bharath Swamy, G.Venkaiah Mohan Babu University School of Computing Department of AI/ML Data Science AndhraPradesh Tirupati India Sri Shanmugha College of Engineering and Technology Department of Artificial Intelligence Salem Tamilnadu Sankari637304 India Sree Vidyanikethan Engineering College Department of Cse Sree Andhra pradesh Tirupati India

ISBN: (纸本)9798350359688

There is a growing trend of using public clouds for image search outsourcing. However, privacy issues arise when image datasets are directly outsourced to untrusted clouds. Recently, a number of secure image retrieval techniques have been put forth. Nevertheless, the majority of them demand input from image owners in order to create secure indexes, wasting a significant amount of their computational power. Numerous approaches are put forth to address this issue;however, their search accuracy is poor when applied to large datasets. We present the first secure image retrieval scheme in this paper that addresses these two issues at the same time to achieve more precise search results. To reduce search time and image owner costs, we allow cloud servers to locally build a reliable hierarchical index graph using the encrypted image features. Moreover, parallel construction and updating of the secure index is possible. We offer a security analysis of the suggested plan. © 2024 IEEE.

关键词： Cloud security

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Confidential machine learning on untrusted platforms:a survey

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Cybersecurity 2021年第1期4卷 461-479页

作者： Sharma Sagar Chen Keke Northwestern Mutual Data Science Associate Professor Director of Trustworthy and Intelligent Computing Lab Department of Computer Science Marquette University Milwaukee WisconsinUSA HP Inc. USA

With the ever-growing data and the need for developing powerful machine learning models,data owners increasingly depend on various untrusted platforms(e.g.,public clouds,edges,and machine learning service providers)for scalable processing or collaborative ***,sensitive data and models are in danger of unauthorized access,misuse,and privacy compromises.A relatively new body of research confidentially trains machine learning models on protected data to address these *** this survey,we summarize notable studies in this emerging area of *** a unified framework,we highlight the critical challenges and innovations in outsourcing machine learning *** focus on the cryptographic approaches for confidential machine learning(CML),primarily on model training,while also covering other directions such as perturbation-based approaches and CML in the hardware-assisted computing *** discussion will take a holistic way to consider a rich context of the related threat models,security assumptions,design principles,and associated trade-offs amongst data utility,cost,and confidentiality.

关键词： Confidential computing Cryptographic protocols Machine learning

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A NOVEL APPROACH FOR PREDICTING EPIDEMIOLOGICAL FORECASTING PARAMETERS BASED ON REAL-TIME SIGNALS AND data ASSIMILATION

arXiv

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arXiv 2023年

作者： Molinas, Romain Casas, César Quilodrán Arcucci, Rossella Şerban, Ovidiu Department of Computing Imperial College London United Kingdom Data Science Institute Imperial College London United Kingdom Data Science Institute Department of Earth Science & Engineering Imperial College London United Kingdom Data Science Institute Department of Computing Imperial College London United Kingdom

This paper proposes a novel approach to predict epidemiological parameters by integrating new real-time signals from various sources of information, such as novel social media-based population density maps and Air Quality data. We implement an ensemble of Convolutional Neural Networks (CNN) models using various data sources and fusion methodology to build robust predictions and simulate several dynamic parameters that could improve the decision-making process for policymakers. Additionally, we used data assimilation to estimate the state of our system from fused CNN predictions. The combination of meteorological signals and social media-based population density maps improved the performance and flexibility of our prediction of the COVID-19 outbreak in London. While the proposed approach outperforms standard models, such as compartmental models traditionally used in disease forecasting (SEIR), generating robust and consistent predictions allows us to increase the stability of our model while increasing its accuracy. © 2023, CC BY-SA.

关键词： COVID-19

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You Are the First Line of Defense: Integrating Humans into Information Security Frameworks

You Are the First Line of Defense: Integrating Humans into I...

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International Conference on Computer and Applications (ICCA)

作者： Bashayer Abdulla Al-Mohammed Ali Baydoun Department of Computing and Data Science Oryx Universal College in Partnership with Liverpool John Moores University Doha Qatar

ISBN: (数字)9798350367560

ISBN: (纸本)9798350367577

This research aimed to understand the current level of security training and cultural awareness among employees across different organizations. The research draws on data collected through a comprehensive questionnaire between employees, providing insights into the relationship between demographic factors and information security awareness. The findings highlight the need for tailored training programs incorporating cultural nuances to mitigate risks effectively. This research contributes to developing adaptable and relevant information frameworks, emphasising users' critical role as the first line of defence against cyber threats. The findings of this research will benefit various stakeholders, such as Governments, semi-governments, and private and non-government organizations.

关键词： Training Phishing Government Cultural differences Stakeholders Resilience Non-governmental organizations

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Multi-Agent System based Medical Diagnosis Using Particle Swarm Optimization in Healthcare

Multi-Agent System based Medical Diagnosis Using Particle Sw...

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2023 International Conference on Artificial Intelligence and Smart Communication, AISC 2023

作者： Dhatterwal, Jagjit Singh Singh Naruka, Mahaveer Kaswan, Kuldeep Singh Koneru Lakshmaiah Education Foundation Department of Artificial Intelligence and Data Science AP Vaddeswaram India G L Bajaj Institute of Technology and Management Greater Noida India Galgotias University School of Computing Science and Engineering Greater Noida India

ISBN: (纸本)9798350322309

Healthcare is an area that is challenging, dynamic, and diverse. Healthcare in the twenty-first century brings a variety of difficulties (population growth and rising healthcare costs). Compared to the conventional medical system, agent technology can offer superior healthcare for that. Agents in the hospital might deal with a variety of medical issues. We list collaboration across hospital wards, the development of diagnostics, the gathering of patient data, etc. as examples of issues that arise in hospitals. These issues can be resolved with a cooperative This paper proposes a generic architecture that integrates swarm intelligence with multi-agent healthcare systems to provide therapy as efficiently as possible. © 2023 IEEE.

关键词： Multi agent systems

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