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Routing and Spectrum Allocation in Broadband Quantum Entanglement Distribution

IEEE Journal on Selected Areas in Communications 2025年第5期43卷 1856-1870页

作者： Bali, Rohan Tittelbaugh, Ashley N. Jenkins, Shelbi L. Agrawal, Anuj Horgan, Jerry Ruffini, Marco Kilper, Daniel C. Bash, Boulat A. The Department of Electrical and Computer Engineering Department University of Arizona TucsonAZ United States The School of Computer Science and Statistics CONNECT Center Trinity College Dublin Dublin Ireland The Electronic and Electrical Engineering Department CONNECT Centre Trinity College Dublin Dublin Ireland

We investigate resource allocation for quantum entanglement distribution over an optical network. We characterize and model a network architecture that employs a single broadband quasi-deterministic time-frequency heralded Einstein-Podolsky-Rosen (EPR) pair source, and develop a routing and spectrum allocation scheme for distributing entangled photon pairs over such a network. As our setting allows separately solving the routing and spectrum allocation problems, we first find an optimal polynomial-time routing algorithm. We then employ max-min fairness criterion for spectrum allocation, which presents an NP-hard problem. Thus, we focus on approximately-optimal schemes. We compare their performance by evaluating the max-min and median number of EPR-pair rates assigned by them, and the associated Jain index. We identify two polynomial-time approximation algorithms that perform well, or better than others under these metrics. We also investigate scalability by analyzing how the network size and connectivity affect performance using Watts-Strogatz random graphs. We find that a spectrum allocation approach that achieves higher minimum EPR-pair rate can perform significantly worse when the median EPR-pair rate, Jain index, and computational resources are considered. Additionally, we evaluate the effect of the source node placement on the performance. © 2025 IEEE. All rights reserved.

关键词： Polynomial approximation

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Detection of Economic Crises With Language Models and Comparative Analysis of Simple Time Series Analysis Models and Machine Learning Algorithms on the Stock Market

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IEEE Access 2025年 13卷 83254-83273页

作者： Kotan, Kurban Kotan, Bayram Kirisoglu, Serdar Duzce University Department of Electrical Electronics and Computer Engineering Düzce81620 Turkey Duzce University Engineering Faculty Department of Computer Engineering Düzce81620 Turkey

This study investigates the use of natural language processing language representation models as an early warning system for economic crises, and compares the performance of time series analysis and machine learning models in financial markets before and during the economic crises in order to select the best model. The data used in the research was collected based on the economic crises that occurred in Turkey in December 2021. The aim is to identify an economic crises period by using language representation models for economic news between August 2021 and January 2022. After identifying the economic crises period, short term (1 day), medium term (15 days) and long term (30 days) forecasts were made for the index of thirty companies with the highest trading volume (BIST30) of Borsa Istanbul between 01/01/2021 and 31/12/2021 and performance comparisons were made between the models. The aim is to develop an effective "smart, automatic crises detection and forecasting model selection application". The CHIT algorithm introduced in the study is a new missing data filling algorithm used in time series forecasting comparisons. Since the CHIT algorithm has a high impact on the model performance, this algorithm is used in the pre-processing step and comparisons are made. © 2013 IEEE.

关键词： Natural language processing systems

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Hybrid Deep Learning Approach for Sentiment Analysis on Twitter Data

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Multimedia Tools and Applications 2025年第15期84卷 15271-15292页

作者： Rakshit, Pranati Sarkar, Pronit Roy, Shubhankar Department of Computer Science and Engineering JIS College of Engineering Nadia Kalyani India

Social media is nowadays a vital platform where people can share their feelings about any incident, product, or any issue. Twitter is one of those platforms which are very popular. If we must make use of this to extract some information and to use that to make recommendation systems, to know the mental status of any person or to find the review, we have to perform the sentiment analysis of those tweets. Recently, deep learning approaches emerged as powerful computational models that discover intricate semantic representations of texts automatically from data without feature engineering. This present work discusses how to find the sentiments from a stream of tweets from the twitter, blogging site subjected to pre-processing and classification supported on emotion of the subject matter as "POSITIVE" or "NEGATIVE". We have used a twitter dataset of 1.6 million data consisting of general tweets on various domains with 800,000 POSITIVE and 800,000 NEGATIVE sentiments. We have developed 3 models with Recurrent Neural Network (RNN), Bi-directional RNN, Long Short-Term Memory (LSTM). Our work focuses on the way which will help us to understand the basic sentiment of people which they want to express by their tweets, comment, or blogs. We have obtained highest accuracy of 83.50%, F1 score of 84.60% from our model with LSTM architecture in analysing the sentiment. LSTMs are specifically designed to address the vanishing gradient problem that occurs in traditional RNNs when learning long-term dependencies. In sentiment analysis, understanding the sentiment of a tweet may require capturing dependencies between words that are spread across a potentially long sequence. LSTMs are better at retaining and utilizing information over extended sequences. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Long short-term memory

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Silver Lining in the Fake News Cloud: Can Large Language Models Help Detect Misinformation?

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2025年第1期6卷 14-24页

作者： Kumar, Raghvendra Goddu, Bhargav Saha, Sriparna Jatowt, Adam Indian Institute of Technology Department of Computer Science and Engineering Patna801106 India University of Innsbruck Department of Computer Science Innsbruck6020 Austria

In the times of advanced generative artificial intelligence, distinguishing truth from fallacy and deception has become a critical societal challenge. This research attempts to analyze the capabilities of large language models (LLMs) for detecting misinformation. Our study employs a versatile approach, covering multiple LLMs with few- and zero-shot prompting. These models are rigorously evaluated across various fake news and rumor detection datasets. Introducing a novel dimension, we additionally incorporate sentiment and emotion annotations to understand the emotional influence on misinformation detection using LLMs. Moreover, to extend our inquiry, we employ ChatGPT to intentionally distort authentic news as well as human-written fake news, utilizing zero-shot and iterative prompts. This deliberate corruption allows for a detailed examination of various parameters such as abstractness, concreteness, and named entity density, providing insights into differentiating between unaltered news, human-written fake news, and its LLM-corrupted counterpart. Our findings aspire to furnish a refined framework for discerning authentic news, human-generated misinformation, and LLM-induced distortions. This multifaceted approach, utilizing various prompt techniques, contributes to a comprehensive understanding of the subtle variations shaping misinformation sources. © 2024 IEEE.

关键词： Iterative methods

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AGBUNet: an enhanced CNN-UNET architecture for the prediction of above ground biomass using deep learning

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Neural Computing and Applications 2025年第5期37卷 3809-3826页

作者： Arumai Shiney, S. Geetha, R. Department of Computer Science and Engineering S.A. Engineering College Chennai India

Accurate prediction of above ground biomass (AGB) is critical for monitoring forest health and carbon cycling. It is crucial for understanding and managing forest ecosystems. In this paper, we propose an enhanced framework combining convolutional neural network (CNN) and UNet, termed AGBUNet, specifically designed for predicting AGB using remote sensing data. The framework consists of separate CNNs for processing each type of image, whose outputs are subsequently fused in a UNet architecture to enhance prediction accuracy. These modifications include customized convolutional layers, advanced preprocessing techniques, and a novel integration of data prompts from separate Sentinel-1 and Sentinel-2 image processing streams. The AGBUNet integrates Sentinel-1 and Sentinel-2 images to leverage complementary information from synthetic aperture radar (SAR) and optical sensors. This study underscores the potential of the AGBUNet model for enhancing biomass estimation from remote sensing data, contributing to better forest management and ecological monitoring. The performance measures obtained are compared with the other models, and the following results are obtained as follows for MSE value of 298.25, RMSE value of 15.27 and MAE value of 12.21, and the values are satisfying compared with earlier benchmarks. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Convolutional neural networks

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ResdenseNet: a lightweight dense ResNet enhanced with depthwise separable convolutions and its applications for early plant disease classification

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Neural Computing and Applications 2025年第8期37卷 6305-6326页

作者： Nagpal, Jyoti Goel, Lavika Department of Computer Science & Engineering The NorthCap University Gurugram122017 India Department of Computer Science and Engineering Malaviya National Institute of Technology Rajasthan Jaipur302017 India

In recent years, artificial intelligence has undergone robust development, leading to the emergence of numerous autonomous AI applications. However, a crucial challenge lies in optimizing computational efficiency and reducing training time while maintaining high accuracy with limited hardware resources. This paper introduces ResdenseNet, a model built upon the MobileNet, DenseNet, and ResNet architectures. ResdenseNet combines dense blocks and residual blocks from the DenseNet and ResNet architectures. In these dense blocks, the standard convolutional units are replaced by depthwise separable convolutional units, a significant part of the MobileNet architecture. The experimental outcomes are contrasted with established models and their iterations, including ResNet-50, ResNet-101, MobileNet-V1, MobileNet-V2, DenseNet-121, and DenseNet-169. The proposed model is tested on benchmark and proposed datasets, showcasing its efficiency in reducing computations and accelerating the training process. Emphasizing hyperparameter importance, ResdenseNet, optimized with a growth rate of 64, 6 layers, and ReLU activation, achieves an accuracy of (98.73%) and a F1-score of (98.20%) on the wheat and barley dataset. The results indicate that ResdenseNet significantly decreases the number of parameters to 0.72M and efficiently shortens training time to 5983.54 s. Particularly noteworthy is ResdenseNet’s superiority over other models in terms of having the fewest parameters, the shortest training time, and the highest accuracy, especially when dealing with wheat, barley, and maize datasets. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Plant diseases

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Deep Learning Based Side-Channel Attack Detection for Mobile Devices Security in 5G Networks

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清华大学学报自然科学版（英文版） 2025年第3期30卷 1012-1026页

作者： Amjed A.Ahmed Mohammad Kamrul Hasan Ali Alqahtani Shayla Islam Bishwajeet Pandey Leila Rzayeva Huda Saleh Abbas Azana Hafizah Mohd Aman Nayef Alqahtani Center for Cyber Security Faculty of Information Science and TechnologyUniversiti Kebangsaan MalaysiaBangi 43600Malaysia Imam Alkadhim University College Department of Computer Techniques EngineeringBaghdad 10066Iraq Center for Cyber Security Faculty of Information Science and TechnologyUniversiti Kebangsaan MalaysiaBangi 43600Malaysia Department of Networks and Communications Engineering College of Computer Science and Information SystemsNajran UniversityNajran 61441Saudi Arabia Institute of Computer Science and Digital Innovation UCSI UniversityKuala Lumpur 56000Malaysia Department of Intelligent Systems and Cyber Security Astana IT UniversityAstana 20000Kazakstan Department of Computer Science College of Computer Science and EngineeringTaibah UniversityMadinah 42353Saudi Arabia Department of Electrical Engineering College of EngineeringKing Faisal UniversityAl-Hofuf 31982Saudi Arabia

Mobile devices within Fifth Generation(5G)networks,typically equipped with Android systems,serve as a bridge to connect digital gadgets such as global positioning system,mobile devices,and wireless routers,which are vital in facilitating end-user communication ***,the security of Android systems has been challenged by the sensitive data involved,leading to vulnerabilities in mobile devices used in 5G *** vulnerabilities expose mobile devices to cyber-attacks,primarily resulting from security ***-permission apps in Android can exploit these channels to access sensitive information,including user identities,login credentials,and geolocation *** such attack leverages"zero-permission"sensors like accelerometers and gyroscopes,enabling attackers to gather information about the smartphone's *** underscores the importance of fortifying mobile devices against potential future *** research focuses on a new recurrent neural network prediction model,which has proved highly effective for detecting side-channel attacks in mobile devices in 5G *** conducted state-of-the-art comparative studies to validate our experimental *** results demonstrate that even a small amount of training data can accurately recognize 37.5％of previously unseen user-typed ***,our tap detection mechanism achieves a 92％accuracy rate,a crucial factor for text *** findings have significant practical implications,as they reinforce mobile device security in 5G networks,enhancing user privacy,and data protection.

关键词： Fifth Generation(5G)networks smartphone information leakage Side-Channel Attack(SCA) deep learning

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Efficient Algorithms for Attributed Graph Alignment with Vanishing Edge Correlation

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IEEE Transactions on Information Theory 2025年第6期71卷 4556-4580页

作者： Wang, Ziao Wang, Weina Wang, Lele University of British Columbia Department of Electrical and Computer Engineering VancouverBCV6T 1Z4 Canada University of Michigan Department of Electrical and Computer Engineering Ann ArborMI48109 United States Carnegie Mellon University Computer Science Department PittsburghPA15213 United States

Graph alignment refers to the task of finding the vertex correspondence between two correlated graphs of n vertices. Extensive study has been done on polynomial-time algorithms for the graph alignment problem under the Erdős–Rényi graph pair model, where the two graphs are Erdős–Rényi graphs with edge probability qu, correlated under certain vertex correspondence. To achieve exact recovery of the correspondence, all existing algorithms at least require the edge correlation coefficient ρu between the two graphs to be non-vanishing as n → ∞. Moreover, it is conjectured that no polynomial-time algorithm can achieve exact recovery under vanishing edge correlation ρuu ≥ n-Θ(1). We identify a local tree structure, which incorporates one layer of user information and one layer of attribute information, and apply the subgraph counting technique to such structures. A polynomial-time algorithm is proposed that recovers the vertex correspondence for most of the vertices, and then refines the output to achieve exact recovery. The consideration of attribute information is motivated by real-world applications like LinkedIn and Twitter, where user attributes like birthplace and education background can aid alignment. © 1963-2012 IEEE.

关键词： Graph algorithms

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LLMER: Crafting Interactive Extended Reality Worlds with JSON Data Generated by Large Language Models

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IEEE Transactions on Visualization and computer Graphics 2025年第5期31卷 2715-2724页

作者： Chen, Jiangong Wu, Xiaoyi Lan, Tian Li, Bin Pennsylvania State University Department of Electrical Engineering United States George Washington University Department of Electrical and Computer Engineering United States

The integration of Large Language Models (LLMs) like GPT-4 with Extended Reality (XR) technologies offers the potential to build truly immersive XR environments that interact with human users through natural language, e.g., generating and animating 3D scenes from audio inputs. However, the complexity of XR environments makes it difficult to accurately extract relevant contextual data and scene/object parameters from an overwhelming volume of XR artifacts. It leads to not only increased costs with pay-per-use models, but also elevated levels of generation errors. Moreover, existing approaches focusing on coding script generation are often prone to generation errors, resulting in flawed or invalid scripts, application crashes, and ultimately a degraded user experience. To overcome these challenges, we introduce LLMER, a novel framework that creates interactive XR worlds using JSON data generated by LLMs. Unlike prior approaches focusing on coding script generation, LLMER translates natural language inputs into JSON data, significantly reducing the likelihood of application crashes and processing latency. It employs a multi-stage strategy to supply only the essential contextual information adapted to the user's request and features multiple modules designed for various XR tasks. Our preliminary user study reveals the effectiveness of the proposed system, with over 80% reduction in consumed tokens and around 60% reduction in task completion time compared to state-of-the-art approaches. The analysis of users' feedback also illuminates a series of directions for further optimization. © 1995-2012 IEEE.

关键词： Coding errors

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Adaptive PPO With Multi-Armed Bandit Clipping and Meta-Control for Robust Power Grid Operation Under Adversarial Attacks

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IEEE Access 2025年 13卷 73586-73602页

作者： Massaoudi, Mohamed Davis, Katherine R. Texas A&M University at Qatar Department of Electrical and Computer Engineering Doha Qatar Texas A&M University Department of Electrical and Computer Engineering College StationTX77843 United States

The seamless and resilient operation of power grids is crucial for ensuring a reliable electricity supply. However, maintaining high operational stability is increasingly challenging due to evolving grid complexities and potential adversarial threats. This paper proposes a novel composite enhanced proximal policy optimization (CePPO) algorithm to improve power grid operation under adversarial conditions. Specifically, our approach introduces three key innovations: 1) multi-armed bandit (MAB) mechanism for dynamic epsilon-clipping that adaptively adjusts exploration-exploitation trade-offs;2) meta-controller framework that automatically tunes hyperparameters including the activation learning rate (ALR) penalties and exploration factors;and 3) integrated gradient-based optimization approach that combines policy gradients with environmental feedback. The effectiveness of the proposed model on the IEEE 14-bus system demonstrates that the CePPO achieves approximately 50% higher average rewards and 51% longer stability periods compared to standard PPO while reducing computational overhead by 35%. CePPO demonstrates superior performance under adversarial attacks compared to baseline approaches. The simulation results validate that CePPO’s adaptive parameter tuning and enhanced exploration strategies make it particularly well-suited for the dynamic nature of power grid control. © 2013 IEEE.

关键词： Electric power transmission networks

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