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The Distributed Intelligent Collaboration to UAV-Assisted VEC: Joint Position Optimization and Task Scheduling

IEEE Internet of Things Journal 2025年第12期12卷 21473-21487页

作者： Yi, Meng Lee, Vincent C.S. Yang, Peng Li, Peisong Zhang, Yifan Wei, Wei Gao, Honghao Southeast University School of Computer Science and Engineering Nanjing211189 China Monash University Faculty of IT Department of DS and AI ClaytonVIC3800 Australia Southeast University School of Computer Science and Engineering the Key Laboratory of Computer Network and Information Integration Ministry of Education Nanjing211189 China Xidian University Hangzhou Institute of Technology Hangzhou311200 China Southeast University-Monash University Joint Graduate School Suzhou215123 China Xi'an University of Technology School of Computer Science and Engineering Xi'an710048 China Shanghai University School of Computer Engineering and Science Shanghai200444 China

Deploying Unmanned Aerial Vehicles (UAVs) as aerial base stations enhances the coverage and performance of communication networks in Vehicular Edge Computing (VEC) scenarios. However, due to the limited communication range and energy capacity of UAVs, they cannot continuously cover entire areas or sustain long flights. Therefore, achieving full communication coverage of a target area with a minimal number of UAVs and efficient task offloading remains a significant challenge. To address this problem, the UAV-assisted Two-stage Intelligent Collaboration (UTIC) method is proposed in this paper to tackle the joint position optimization and task scheduling issue. Firstly, a UAV-assisted Two-stage Task Scheduling (UTTS) system model is designed to optimize the allocation process. Secondly, an Enhanced Particle Swarm Optimization (E-PSO) algorithm is designed to determine the optimal positions of UAVs, ensuring complete coverage of all mobile vehicles (MVs) with the minimum number of UAVs. Thirdly, Deep Deterministic Policy Gradient (DDPG) method is employed to find the optimal scheduling decisions for MVs, considering energy consumption, delay, and task priorities. Simulation results demonstrate that the proposed UTIC method can achieve nearly 20% reduction in UAV deployment and outperform three other classical reinforcement learning (RL) algorithms in terms of reducing system cost. © 2025 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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Style Knowledge Graph: Augmenting Text Style Transfer with Knowledge Graphs 31

Style Knowledge Graph: Augmenting Text Style Transfer with K...

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2025 Workshop on Generative AI and Knowledge Graphs, GenAIK 2025, co-located with the 31st International Conference on Computational Linguistics, COLING 2025

作者： Toshevska, Martina Kalajdziski, Slobodan Gievska, Sonja Faculty of Computer Science and Engineering Ss. Cyril and Methodius University Skopje Macedonia

ISBN: (纸本)9798891762121

Text style transfer is the task of modifying the stylistic attributes of a given text while preserving its original meaning. This task has also gained interest with the advent of large language models. Although knowledge graph augmentation has been explored in various tasks, its potential for enhancing text style transfer has received limited attention. This paper proposes a method to create a Style Knowledge Graph (SKG) to facilitate and improve text style transfer. The SKG captures words, their attributes, and relations in a particular style, that serves as a knowledge resource to augment text style transfer. We conduct baseline experiments to evaluate the effectiveness of the SKG for augmenting text style transfer by incorporating relevant parts from the SKG in the prompt. The preliminary results demonstrate its potential for enhancing content preservation and style transfer strength in text style transfer tasks, while the results on fluency indicate promising outcomes with some room for improvement. We hope that the proposed SKG and the initial experiments will inspire further research in the field. ©2025 International Committee on Computational Linguistics (ICCL)

关键词： Knowledge graph

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Implications of the forthcoming forensic sciences standard ISO/IEC 21043 for forensic biometrics 12

Implications of the forthcoming forensic sciences standard I...

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12th International Workshop on Biometrics and Forensics, IWBF 2024

作者： Meuwly, Didier University of Twente Faculty of Electrical Engineering Mathematics and Computer Science Enschede Netherlands

ISBN: (纸本)9798350354478

The forthcoming forensic sciences standard ISO/IEC 21043 is a methodological and technical standard, currently at the stage of Draft International Standard. When adopted, it will apply to all forensic disciplines, including forensic biometrics. This standard has been designed by forensic, legal, and standardisation professionals to ensure that forthcoming forensic services, products, and systems are safe, reliable, and consistently perform as intended. The aim of this contribution is to present to the forensic biometric community some of the implications of adopting ISO/IEC 21043 for the forensic biometric *** an introduction (§ I) and a review of the existing international standards applied in forensic science (§ II), the five-part structure of ISO/IEC 21043 is described: 1. Terms and definitions, 2. Recovery, 3. Analysis, 4. Interpretation and 5. Reporting, (§ III). Then a selection of requirements and recommendations of this five-part standard are highlighted, and their implication for forensic biometric research, development, implementation, and practice is discussed (§ IV).The conclusion is that the adoption and use of ISO/IEC 21043 will help forensic laboratories to reach accreditation and to enhance their existing quality management systems. It will also make it easier for forensic biometric researchers to develop, deliver, and implement more relevant, more reliable, and safer services, products, and systems to the forensic biometric examiners performing casework (§ V). © 2024 IEEE.

关键词： Forensic science

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Chain of Thought Prompting in Vision-Language Model for Vision Reasoning Tasks 37th

Chain of Thought Prompting in Vision-Language Model for V...

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37th Australasian Joint Conference on Artificial Intelligence, AJCAI 2024

作者： Ou, Jianjiu Zhou, Jianlong Dong, Yifei Chen, Fang School of Computer Science Institute Faculty of Engineering and IT University of Technology Sydney Ultimo Australia

ISBN: (纸本)9789819603503

The large language model has demonstrated its ability to reason and interpret in text-to-text applications. Current Chain of Thought (CoT) research focuses on either explaining reasoning steps or improving prediction results. This paper proposes a transformer-based language model to generate explanations as the Chain of Thoughts, then fuse visual (image) modality. The proposed model allows a multimodal reasoning chain based on text and image information. In particular, we adopt an attention layer with a gated fusion mechanism to form a joint representation for image and text input. The experiment results show that using CoT as part of the input can increase answer prediction by 19% in our study. Therefore, in our modelling process, CoT prompting is not only a part of the output to generate reasoning explanations but is also used as the input of the decoder to predict answers. The proposed method outperforms GPT-3.5 (two-shot) by 14% and GPT-4 (CoT) by 4.71% with a much smaller parameter size pre-trained language model. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Visual languages

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Bitcoin Price Prediction Using Deep Learning Models 4

Bitcoin Price Prediction Using Deep Learning Models

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4th International Conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Hajare, Rasika Puri, Chetan Gote, Pradnyawant M. Faculty of Engineering and Technology Department of Computer Science and Design Maharashtra Wardha442001 India

ISBN: (纸本)9798331523923

Forecasting the price of bitcoins is significant in contemporary research, given the fact that the digital currency is relatively unpredictable and highly integrated in global securities markets. This paper compares the use of three deep learning models, namely recurrent neural networks (RNN), gated recurrent unit (GRU), and a convolutional neural network-long short-term memory (CNN-LSTM) for Bitcoin price prediction. Historical Bitcoin price data is used in the dataset, and every one of the models is trained and tested based on its performance to predict future prices based on past data. The proposed models include the RNN and GRU, which incorporate sequential dependency mappings to learn complex sequences, and the CNN-LSTM structures combine convolution layers for feature extraction and LSTM layers for sequencing. Different models' competency is discussed using the averaged Mean Squared Error (MSE) and graphs of the model's prediction against actual prices. The experimental results reveal that the proposed CNN-LSTM model has better prediction accuracy than RNN and GRU in time-series forecasting tasks, indicating that more complex architecture hybrid models might provide better forecasting performance. In analyzing and analyzing cryptocurrency price dynamics, this investigation gives information about the opportunities and challenges of these models to enhance future research. © 2025 IEEE.

关键词： Prediction models

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Accuracy Prediction using Machine Learning Techniques for Indian Patient Liver Disease

Accuracy Prediction using Machine Learning Techniques for In...

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2023 International Conference on Computational Intelligence and Sustainable engineering Solution, CISES 2023

作者： Ds, Shylaja Karunya, Mekala Oliviya, J. Suriya Prakash, J. Jain University Computer Science Engineering Faculty of Engineering and Technology Bengaluru India

ISBN: (纸本)9798350323917

One of the principal cause of mortality in the countryis liver disease. Each year, liver disease kills more than 2.4 percent of all Indians. As a result,for the early intervention of liver disease, anautomated programme with higher accuracy and reliability is necessary. Specific machine learning models such as SVM, Logistic Regression, KNN, and ANN are being created for this purpose in order to foretell liver disease with more precision, accuracy, and reliability. © 2023 IEEE.

关键词： Neural networks

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FeFET Based Logic-in-Memory Pipeline-Style Circuits 17

FeFET Based Logic-in-Memory Pipeline-Style Circuits

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17th IEEE International Conference on Solid-State and Integrated Circuit Technology, ICSICT 2024

作者： Li, Yang Yang, Zhaohui Xia, Yinshui Ningbo University Faculty of Electrical Engineering and Computer Science Ningbo315211 China

ISBN: (纸本)9798350361834

Emerging non-volatile memory technologies provides a solution to break through the limitations of the vonNeumann architecture and using these technologies to develop logic-in-memory circuits. In this paper, we propose a novel circuit design style based on hafnium oxide ferroelectric memory technology, using a accumulator as an example. This design method offers a new direction for the development of logic-in-memory circuits. Additionally, the method significantly increases data processing capability and provides greater flexibility for circuit design. © 2024 IEEE.

关键词： Ferroelectric RAM

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Harmful Brain Activity Classification Using Ensemble Deep Learning 66

Harmful Brain Activity Classification Using Ensemble Deep Le...

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66th International Symposium ELMAR, ELMAR 2024

作者： Habijan, Marija Sojo, Robert Tolic, Ivana Hartmann Galic, Irena Faculty of Electrical Engineering Computer Science and Information Technology Osijek Osijek Croatia

ISBN: (纸本)9798350375411

Electroencephalography (EEG) is a crucial tool for monitoring electrical brain activity and diagnosing neurological conditions. Manual analysis of EEG signals is time-consuming and prone to variability, necessitating automated methods for accurate and efficient interpretation. This paper presents an ensemble learning approach for the automated classification of harmful brain activity from EEG data. The model combines the strengths of ResNet1d GRU, EfficientNetB0, and EfficientNetB1 to predict brain activity from spectrograms and raw EEG signals. By leveraging the unique capabilities of each model and employing a weighted averaging scheme, the ensemble achieves a competitive Kullback-Liebler divergence score of 0.3 on the testing dataset. This approach demonstrates the potential of ensemble learning to improve the accuracy and robustness of automated EEG signal classification, offering a valuable tool for early detection and diagnosis of neurological conditions. © 2024 IEEE.

关键词： Contrastive Learning

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Optimistic Online Non-stochastic Control via FTRL 63

Optimistic Online Non-stochastic Control via FTRL

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63rd IEEE Conference on Decision and Control, CDC 2024

作者： Mhaisen, Naram Iosifidis, George Delft University of Technology Faculty of Electrical Engineering Mathematics and Computer Science Netherlands

ISBN: (纸本)9798350316339

This paper brings the concept of 'optimism' to the new and promising framework of online Non-stochastic Control (NSC). Namely, we study how NSC can benefit from a prediction oracle of unknown quality responsible for forecasting future costs. The posed problem is first reduced to an optimistic learning with delayed feedback problem, which is handled through the Optimistic Follow the Regularized Leader (OFTRL) algorithmic family. This reduction enables the design of OptFTRL-C, the first Disturbance Action Controller (DAC) with optimistic policy regret bounds. These new bounds are commensurate with the oracle's accuracy, ranging from O (1) for perfect predictions to the order-optimal O(ST) even when all predictions fail. By addressing the challenge of incorporating untrusted predictions into online control, this work contributes to the advancement of the NSC framework and paves the way toward effective and robust learning-based controllers. © 2024 IEEE.

关键词： Stochastic control systems

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Discover Image Restoration: Analysis and Novel Architecture Proposal 7

Discover Image Restoration: Analysis and Novel Architecture ...

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7th International Conference on Inventive Computation Technologies, ICICT 2024

作者： Pawar, Poonam Ainapure, Bharati Vishwakarma University Faculty of Science And Technology Dept of Computer Engineering Pune India

ISBN: (纸本)9798350359299

Digital image quality is essential for a variety of applications in visual information processing, including medical diagnostics, defence, and many other applications. However, there are several challenges that must be overcome before an image can be used, and these often result in noise, distortion, and degradation. The field of image restoration is growing in significance as it focuses on repairing and restoring damaged images. With the increasing number of imaging equipment and the wide range of applications, image restoration techniques are becoming more and more important. Because of the challenges posed by low light levels, sensor noise, and other environmental limitations, more analysis on restoration strategies is needed. This study provides researchers, practitioners, and students with a clear understanding of image restoration techniques by providing an extensive overview of the developments in different fields. This research study offers a comprehensive analysis by analysing the development of these techniques, from traditional methodologies to the most recent deep learning approaches. Here we have proposed a novel architecture for image restoration, designed to facilitate seamless implementation and achieve high-quality results. © 2024 IEEE.

关键词： Image reconstruction

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