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arXiv 2023年

作者： Chu, Nam H. Nguyen, Diep N. Hoang, Dinh Thai Phan, Khoa T. Dutkiewicz, Eryk Niyato, Dusit Shu, Tao School of Electrical and Data Engineering University of Technology Sydney Australia School of Engineering and Mathematical Sciences La Trobe University Melbourne Australia School of Computer Science and Engineering Nanyang Technological University Singapore Department of Computer Science and Software Engineering Auburn University United States

This work proposes a novel framework to dynamically and effectively manage and allocate different types of resources for Metaverse applications, which are forecasted to demand massive resources of various types that have never been seen before. Specifically, by studying functions of Metaverse applications, we first propose an effective solution to divide applications into groups, namely MetaInstances, where common functions can be shared among applications to enhance resource usage efficiency. Then, to capture the real-time, dynamic, and uncertain characteristics of request arrival and application departure processes, we develop a semi-Markov decision process-based framework and propose an intelligent algorithm that can gradually learn the optimal admission policy to maximize the revenue and resource usage efficiency for the Metaverse service provider and at the same time enhance the Quality-of-Service for Metaverse users. Extensive simulation results show that our proposed approach can achieve up to 120% greater revenue for the Metaverse service providers and up to 178.9% higher acceptance probability for Metaverse application requests than those of other baselines. Copyright © 2023, The Authors. All rights reserved.

关键词： Reinforcement learning

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Optimizing Video Streaming Costs in the Cloud: A CNN Approach

Optimizing Video Streaming Costs in the Cloud: A CNN Approac...

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Information Technology and Computing (ICITCOM), International Conference on

作者： Mahmoud Darwich Kasem Khalil Magdy Bayoumi Department of Mathematics and Computer Science University of Mount Union Alliance OH Department of Electrical and Computer Engineering University of Mississippi MS Department of Electrical and Computer Engineering University of Louisiana Lafayette LA

As demand for high-quality, on-demand content skyrockets, cost optimization of video streaming in cloud en-vironments has become critical. Addressing this issue, our research introduces Deep Stream Cost (DSC), an innovative deep learning architecture for optimizing cloud-based video streaming costs. DSC employs convolutional neural networks (CNNs), dynamic resource scaling, and task offloading strategies, achieving considerable cost reduction while ensuring optimal video analytics performance. Our results are promising: system efficiency improved by up to 40%, resource usage was curtailed by 30%, and latency was reduced by 25%, all of which are substantial gains over traditional methods. Beyond cost reduction, DSC enhances scalability and availability, thanks to its real-time monitoring, predictive resource provisioning, and cost-awareness feedback loop. DSC's contribution to the development of cost-effective and scalable video delivery systems is highly relevant in today's era of cloud-based services and increasing demand for efficient resource utilization.

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Enhancing Video Storage Efficiency in the Cloud: Machine Learning-Driven Optimization Strategies

Enhancing Video Storage Efficiency in the Cloud: Machine Lea...

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Information Technology and Computing (ICITCOM), International Conference on

Video storage optimization in the cloud is a critical research area with significant implications for efficient resource utilization, cost reduction, and improved user experiences. This research manuscript explores the application of machine learning techniques for video storage optimization in cloud-based environments. The objective is to develop innovative methods that leverage the power of machine learning algorithms to enhance the efficiency of video storage, enabling effective management, retrieval, and delivery of video content. The manuscript presents a comprehensive analysis of existing approaches, identifies their limitations, and proposes novel techniques to address the identified challenges. The research focuses on leveraging machine learning algorithms for video transcoding, content-aware caching, intelligent data placement, and adaptive streaming, among other areas. Experimental evaluations and performance comparisons are conducted to validate the effectiveness of the proposed techniques. The findings show that the proposed CNN-based video transcoding technique achieved an average storage size reduction of 40%. The content-aware caching technique outperformed baseline methods with an average cache hit rate of 75%. The RL-based intelligent data placement technique achieved an average data popularity of 85%. lastly, the RL-based adaptive streaming technique obtained significantly higher user satisfaction ratings, with an average Mean Opinion Score (MOS) of 4.6 out of 5.

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Learning-Based Energy Efficiency Optimization in Cell-Free Massive MIMO

Learning-Based Energy Efficiency Optimization in Cell-Free M...

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IEEE Conference on Global Communications (GLOBECOM)

作者： Guangchen Wang Peng Cheng Zhuo Chen Branka Vucetic Yonghui Li School of Electrical and Information Engineering The University of Sydney Australia Department of Computer Science and Information Technology La Trobe University Australia Data 61 CSIRO Australia

Cell-free massive multiple-input multiple-output (MIMO) deploys a large number of distributed access points (APs) without cell edges, offering seamless connectivity with significantly increased spectral efficiency and system capacity, but suffering degraded energy efficiency. In this paper, we develop a green energy scheme by simultaneously optimizing power allocation and AP selection. We formulate it as a non-convex mixed-integer nonlinear programming problem (MINLP), which is NP-hard. To address this challenging problem, we propose a learning-based algorithm that embeds non-convex optimization into contemporary deep reinforcement learning (DRL), referred to as optimization-embedded soft actor-critic with graph transformer networks (OSAC-G). OSAC-G enjoys the benefits of directly online inferring solutions for the non-convex problem with a much lower computational complexity compared to conventional non-convex optimization. Simulation results demonstrate that the green energy scheme significantly decreases energy consumption compared to the existing ones.

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Inverse Reinforcement Learning with Graph Neural Networks for IoT Resource Allocation

Inverse Reinforcement Learning with Graph Neural Networks fo...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Guangchen Wang Peng Cheng Zhuo Chen Wei Xiang Branka Vucetic Yonghui Li School of Electrical and Information Engineering The University of Sydney Australia Department of Computer Science and Information Technology La Trobe University Australia Data 61 CSIRO Australia

The rapid development of Internet of Things (IoT) applications requires efficient computing and communication resource allocation strategies to streamline the existing network operations. These strategies could be formulated as mixed-integer nonlinear programming (MINLP) problems, where the optimal branch-and-bound (B&B) with the full strong branching (FSB) variable selection policy features an extremely high complexity. We propose inverse reinforcement learning with graph neural networks (GNNIRL) to generate a new variable selection policy that closely matches the FSB variable selection. Without sacrificing the optimality, the GNNIRL can directly infer the variable selection with a significantly lower complexity, which is also verified by simulation.

关键词： Input variables Signal processing algorithms Reinforcement learning Signal processing Programming Graph neural networks Internet of Things

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ChatGPT and Generative AI Guidelines for Addressing Academic Integrity and Augmenting Pre-Existing Chatbots

ChatGPT and Generative AI Guidelines for Addressing Academic...

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IEEE International Conference on Industrial Technology (ICIT)

作者： Daswin De Silva Nishan Mills Mona El-Ayoubi Milos Manic Damminda Alahakoon Centre for Data Analytics and Cognition La Trobe University Melbourne Australia Education Services La Trobe University Melbourne Australia Department of Computer Science Virginia Commonwealth University Richmond USA

Chat Generative Pretrained Transformer (Chat-GPT) and related Generative AI models are leading a paradigm shift in the acceptance and application of Artificial Intelligence (AI) across all disciplines and industry sectors. Despite the criticisms of an ‘intelligence without knowledge or reasoning or the notions of truth’, ChatGPT is highly effective at human-like conversation with seemingly sophisticated and useful responses to questions, summarization, classification, extraction and generation tasks. Unlike similar large AI models in the modalities of image, audio and video, text-based conversation is straightforward and familiar to a large audience of regular users of the Internet and smartphone applications. This is further accentuated by the large-scale adoption of ‘standard’ chatbot technologies for trivial conversations in task-specific automation, across every industry sector. This rare combination of highly effective human-like conversation, familiarity of foundational technology and versatility of intelligent application, has led to several challenges and opportunities in leveraging generative AI. A primary challenge is its impact on the academic integrity of scholarly work, where AI-generated content can be useful and detrimental in both teaching and research. On the other hand, ChatGPT presents a unique opportunity in augmenting preexisting (‘standard’) chatbots with human-like conversation for advanced intelligent automation, across all application domains. Although diametrically opposed, the challenge of addressing academic integrity and the opportunity of augmenting pre-existing chatbots are grounded in the conversational AI capabilities of ChatGPT and similar generative AI models. In this paper, we investigate these formative capabilities and present guidelines for leveraging ChatGPT and similar generative AI models.

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Leveraging Resource-Aware Deep Collaborative Learning Towards Secure B5G-Driven IoT-Fog-Based Consumer Electronic Systems

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IEEE Transactions on Consumer Electronics 2024年 1-1页

作者： Tripathy, Subhranshu Sekhar Bebortta, Sujit Chakraborty, Chinmay Senapati, Dilip Pani, Subhendu Kumar Guduri, Manisha School of Computer Engineering KIIT Deemed to be University Bhubaneswar India Department of Computer Science Ravenshaw University Cuttack Odisha India Department of Electronics and Communication Engineering Birla Institute of Technology Jharkhand India Department of Computer Science University of Delhi Delhi India Kausalya Ganga Bhubaneswar Odisha India School of Computing and Informatics University of Louisiana at Lafayette LA USA

The integration of Fog computing and 5G communication may enhance Cyber Physical Systems (CPSs) for effective time identification of cyber attacks among consumer electronics. An unsupervised Intrusion Detection System (IDS) based on Generative Adversarial Networks (GANs) and Recurrent Neural Networks (RNNs) is presented in this study. This system is tailored to the resource limitations of consumer electronics within CPSs. By leveraging the processing power of fog nodes and the low-latency capabilities of 5G networks, cyber attacks can be swiftly identified. The use of a trained Long Short-Term Memory (LSTM) network encoder improves detection rates by enhancing reconstruction loss computation. Experimental results demonstrate that this approach, implemented through distributed fog computing infrastructure, offers better detection rates with a 15.2% reduction in detection latency and a 24.2% decrease in overall energy consumption compared to baseline methods. This innovative system could serve as an effective alternative for securing consumer electronic devices integrated into CPSs. IEEE

关键词： Fog computing

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Privacy-preserving cooperative localization in vehicular edge computing infrastructure

Privacy-preserving cooperative localization in vehicular edg...

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作者： Chandra Shit, Rathin Sharma, Suraj Watters, Paul Yelamarthi, Kumar Pradhan, Biswajeet Davison, Richard Morgan, Graham Puthal, Deepak Department of Computer Science and Engineering International Institute of Information Technology Bhubaneswar India Department of Computer Science and Information Technology La Trobe University MelbourneVIC Australia School of Engineering and Technology Central Michigan University Mount PleasantMI United States School of Information Systems and Modelling University of Technology Sydney UltimoNSW Australia School of Computing Newcastle University Newcastle upon Tyne United Kingdom

Advancement of computing and communication techniques transforms the traditional transport system into the intelligent transportation system (ITS). The development of distributed computing in a vehicular network platform also called Vehicular Edge Computing (VEC) promise to address most of the challenges faced by the ITS. Localization is important in these vehicular networks because of its key contribution in autonomous driving, smart traffic monitoring, and collision avoidance services. For localization, current GPS and hybrid methods are in-efficient because of GPS outage in urban infrastructure and dynamic nature of the vehicular networks. The cooperative localization approaches, on the other hand, use dedicated short range communication to broadcast messages and estimate location. However, these messages are un-encrypted and periodic which gives a privacy risk for vehicles. This article presents a privacy-preserving cooperative localization in vehicular network based upon dynamic pseudonym changing strategy. First, the localization delay is addressed with the implementation of dynamic vehicular edge assignment for computational task management. In the next step, the localization is estimated from the neighbor and road side unit ranging measurement followed by a real-time prediction of the vehicle. The performance of the proposed algorithms is analyzed in terms of localization accuracy and privacy preservation strength. Furthermore, the proposed method is simulated in a real city scenario followed by localization accuracy and privacy analysis. Finally, the localization accuracy and privacy strength of the proposed approach are compared with the state-of-the-art methods. © 2020 John Wiley & Sons, Ltd.

关键词： Edge computing

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PPILS: Protein-protein interaction prediction with language of biological coding

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computers in Biology and Medicine 2025年 186卷 109678-109678页

作者： Howladar, Nayan Kabir, Md Wasi Ul Hoque, Foyzul Katebi, Ataur Hoque, Md Tamjidul Department of Computer Science University of New Orleans New OrleansLA United States Department of Computer Science & Engineering Independent University Bangladesh Department of Bioengineering Northeastern University BostonMA United States Center for Theoretical Biological Physics Northeastern University BostonMA United States

Protein-protein interactions within a cell are essential for various fundamental biological processes. Computational techniques have arisen in bioinformatics due to the challenging and resource-intensive nature of experimental protein pair interaction studies. This research seeks to create a cutting-edge machine learning method for predicting protein pair interactions using carefully chosen input features and leveraging evolutionary data. PPILS leverages evolutionary knowledge from the protein language model. It develops an encoder-decoder architecture with light attention. The trained model obtains protein embeddings from a language model and employs a light attention-based encoder, where a single convolution operation generates attention. A subsequent convolution is applied to input features, creating a representative construct for the protein interaction prediction. These encoded representations are then channeled into the decoder to predict protein interactions. Our findings indicated that PPILS outperformed existing methods in PPI prediction. The proposed method could be essential in protein-protein interaction prediction, further accelerating the discovery of protein-based drugs. © 2025

关键词： Decoding

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Optimizing Privacy and Utility Tradeoffs for Group Interests Through Harmonization

Optimizing Privacy and Utility Tradeoffs for Group Interests...

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International Joint Conference on Neural Networks (IJCNN)

作者： Bishwas Mandal George Amariucai Shuangqing Wei Department of Computer Science Kansas State University Manhattan KS USA Division of Electrical & Computer Engineering Louisiana State University Baton Rouge LA USA

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

We propose a novel problem formulation to address the privacy-utility tradeoff, specifically when dealing with two distinct user groups characterized by unique sets of private and utility attributes. Unlike previous studies that primarily focus on scenarios where all users share identical private and utility attributes and often rely on auxiliary datasets or manual annotations, we introduce a collaborative data-sharing mechanism between two user groups through a trusted third party. This third party uses adversarial privacy techniques with our proposed data-sharing mechanism to internally sanitize data for both groups and eliminates the need for manual annotation or auxiliary datasets. Our methodology ensures that private attributes cannot be accurately inferred while enabling highly accurate predictions of utility features. Importantly, even if analysts or adversaries possess auxiliary datasets containing raw data, they are unable to accurately deduce private features. Additionally, our data-sharing mechanism is compatible with various existing adversarially trained privacy techniques. We empirically demonstrate the effectiveness of our approach using synthetic and real-world datasets, showcasing its ability to balance the conflicting goals of privacy and utility.

关键词： Data privacy Privacy Accuracy Annotations Neural networks Collaboration Manuals

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