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IEEE Conference on Wireless Communications and Networking

作者： Houze Feng Jingjing Wang Jianrui Chen Xiangwang Hou Jiacheng Wang Geng Sun Dusit Niyato School of Cyber Science and Technology Beihang University Beijing China Department of Electronic Engineering Tsinghua University Beijing China College of Computing and Data Science Nanyang Technological University Singapore College of Computer Science and Technology Jilin University Changchun China

ISBN: (数字)9798350368369

ISBN: (纸本)9798350368376

Flying ad hoc networks (FANETs) composed of small unmanned aerial vehicles (UAVs) are flexible, inexpensive, and fast to deploy, which have been used in an increasing number of mission scenarios. However, unstable link quality and frequently changing network topology pose significant challenges for adopting existing routing protocols in mobile ad hoc networks (MANETs). In this paper, we propose a time-slotted on-demand predictive (TSDP) routing protocol designed specifically for UAV networks. The TSDP protocol introduces a novel approach to route selection by incorporating multiple criteria, including delivery ratio, adjacent degree, and mobility prediction factor, to ensure reliable and efficient data transmission. By addressing high latency in route discovery and excessive broadcast overhead, TSDP employs a time-slotted communication mechanism that reduces packet drop rates and enhances route stability. Simulation results demonstrate that TSDP consistently outperforms ad hoc on-demand distance vector (AODV) and dynamic source routing (DSR) protocols in terms of throughput, packet delivery ratio, end-to-end delay, and overhead, particularly in highly dynamic network environments.

关键词： Simulation Stability criteria Autonomous aerial vehicles Throughput Routing Routing protocols Vectors Delays Vehicle dynamics AODV

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CSAKD: Knowledge Distillation with Cross Self-Attention for Hyperspectral and Multispectral Image Fusion

arXiv

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arXiv 2024年

作者： Hsu, Chih-Chung Ni, Chih-Chien Lee, Chia-Ming Kang, Li-Wei Institute of Data Science Miin Wu School of Computing National Cheng Kung University Tainan Taiwan Department of Electrical Engineering National Taiwan Normal University Taipei Taiwan

Hyperspectral imaging, capturing detailed spectral information for each pixel, is pivotal in diverse scientific and industrial applications. Yet, the acquisition of high-resolution (HR) hyperspectral images (HSIs) often needs to be addressed due to the hardware limitations of existing imaging systems. A prevalent workaround involves capturing both a high-resolution multispectral image (HR-MSI) and a low-resolution (LR) HSI, subsequently fusing them to yield the desired HR-HSI. Although deep learning-based methods have shown promising in HR-MSI/LR-HSI fusion and LR-HSI super-resolution (SR), their substantial model complexities hinder deployment on resource-constrained imaging devices. This paper introduces a novel knowledge distillation (KD) framework for HR-MSI/LR-HSI fusion to achieve SR of LR-HSI. Our KD framework integrates the proposed Cross-Layer Residual Aggregation (CLRA) block to enhance efficiency for constructing Dual Two-Streamed (DTS) network structure, designed to extract joint and distinct features from LR-HSI and HR-MSI simultaneously. To fully exploit the spatial and spectral feature representations of LR-HSI and HR-MSI, we propose a novel Cross Self-Attention (CSA) fusion module to adaptively fuse those features to improve the spatial and spectral quality of the reconstructed HR-HSI. Finally, the proposed KD-based joint loss function is employed to co-train the teacher and student networks. Our experimental results demonstrate that the student model not only achieves comparable or superior LR-HSI SR performance but also significantly reduces the model-size and computational requirements. This marks a substantial advancement over existing state-of-the-art methods. The source code is available at https://***/ming053l/CSAKD. © 2024, CC BY.

关键词： Image fusion

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Leveraging Deep Learning Models for Machine-Generated Text Detection Using Transformer-Based Models

Leveraging Deep Learning Models for Machine-Generated Text D...

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Innovative computing, Intelligent Communication and Smart Electrical Systems (ICSES), International Conference on

作者： P. Suthanthiradevi G Revathy K. Rejini Muthu Lakshmi.V Department of Data Science and Business Systems School of Computing SRM institute of Science and Technology Kattankulathur Chennai Department of Computer science and engineering Vels Institute of science Technology and Advanced studies Chennai Department of Computer Science Engineering Amrita college of Engineering and Technology Nagercoil Department of Computer Science and Engineering St. Joseph's College of Engineering OMR Chennai

ISBN: (数字)9798331543624

ISBN: (纸本)9798331543631

GPT is a large language model (LLM) derived from natural language processing that can generate a human-like text using machine learning. However, these models raise questions about authenticity and reliability of material, particularly in fields such as journalism, social media, and academia, despite their usefulness for automating text-based tasks. Detecting machine-generated text is thus an important difficulty in ensuring content integrity. This study investigates the use of huge language models as a technique for recognizing machine-generated material. The author proposes a comprehensive detection model by evaluating the language patterns, syntactic structures, and stylistic traits that separate AI-generated literature from human writing. In addition, this research investigate the possibilities of fine-tuning models designed expressly for text identification tasks and evaluate their performance using LLM - Detect AI Generated Text datasets. In digital ecosystems, LLMs are effective at detecting AI-generated text, providing a novel approach for content moderation, academic integrity checks, and synthetic media detection. An increasingly AI-powered future will require a model that can discriminate between human and machine-generated writing in real-time. According to experimental findings, the CNN architecture's design combined with the use of DistilBERT embeddings allows for the effective and efficient classification of AI generated text data, achieving an exceptional 98% accuracy rate.

关键词： Deep learning Deepfakes Accuracy Social networking (online) Biological system modeling Large language models Text detection Writing Transformers Natural language processing

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Utilizing Web Scraping and Machine Learning for Improving Accuracy of Smartphone Price Prediction

Utilizing Web Scraping and Machine Learning for Improving Ac...

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Advances in data Engineering and Intelligent computing Systems (ADICS), International Conference on

作者： V. Vasanth Kumar C. TamizhSelvan L. Manoharan R. Vishnu Sekhar V. Vinoth Kumar Department of Electronics and Communication Engineering Hindustan Institute of Technology and Science Chennai India Department of Data Science and Business Systems School of Computing SRM Institute of Science and Technology India Department of Computer Science and Engineering Hindustan Institute of Technology and Science Chennai India Department of Biotechnology Integrated Bioprocessing Laboratory School of Bioengineering SRM Institute of Science and Technology India

ISBN: (数字)9798350364828

ISBN: (纸本)9798350364835

Mobile prices play a pivotal role in determining their popularity amongst consumers and their competitive standing within the market. As customers consider their budget while evaluating a mobile phone's specifications and design, estimating the price beforehand becomes essential to cater to their needs and expectations. Hence, accurately forecasting mobile phone prices is a vital step in the product launch process to remain competitive and assess the market dynamics and competitors. The primary aim of this research is to determine the true market value of mobile phones. In this research, the dataset was extracted by means of utilizing a web scraping process from the flipkart website. The main objective of this research is to fetch the most recent dataset based on the most recent features. The extracted dataset is fitted into regression models such as linear regression, decision tree regressor, random forest regressor, KNN regressor, and gradient boosting regressor and performs the comparative analysis. The models are evaluated based on the mean absolute error metric.

关键词： Measurement Computational modeling Linear regression Predictive models Feature extraction Boosting data models

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Ensuring Web Application Security: An OWASP Driven Development Methodology

Ensuring Web Application Security: An OWASP Driven Developme...

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

作者： Sakthivel, M. Sivanantham, S. Bharathiraja, N. Bala Krishna, N. Kamalraj, R. Kumar, V. Saravana Sri Shanmugha College of Engineering and Technology Department of Artificial Intelligence and Data Science Salem Sankari India Saveetha Institute of Medical and Technical Sciences Department of Cse Saveetha School of Engineering Chennai India Vel Tech Multi Tech Dr. Rangarajan Dr. Sakunthala Engineering College Department of Biomedical Engineering Chennai India Mohan Babu University School of Computing Department of Ai&ml Andhra Pradesh Tirupati India University School of Cs and It Jayanagar Bangalore India Sreenidhi Institute of Science and Technology Department of It Hyderabad India

ISBN: (纸本)9798350359688

Website security is a big threat for both large companies and individual developers, the more advanced the technology employed, the more challenging it is to build secure website development procedures. Hackers can easily access vulnerabilities that were not patched during development and that were thereafter distributed. This could result in significant financial losses for the business or the person. Users who visit vulnerable websites may be subject to XSS attacks that could compromise their systems, or an unsecure database configuration may result in a potential data leak, in which case the password of every user who has registered on the website is compromised. Users who use the same password on numerous websites run the danger of identity theft. This study offers the most common vulnerabilities that should be fixed in any application, giving developers the crucial place to start. The overwhelming number of vulnerabilities in each application that is being developed served as the impetus for this project. Every developer, regardless of expertise level, needs a place to start in order to fix any vulnerabilities that may have appeared in their program. The goal of this project is to create a safe online application in accordance with the recommendations of the Open Web Application Security Project (OWSAP). This demonstrates how web application vulnerabilities can be mitigated through coding changes, configuration adjustments, and patch applications. The top ten vulnerabilities according to OWASP are SQL injections, faulty authentication, sensitive data exposure, faulty access control, and XML external entities. The web application's security has been examined and shown to have defenses in place for those vulnerabilities. © 2024 IEEE.

关键词： Information leakage

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Detection of Abdominal and Mediastinal Lymph Nodes in Computed Tomography Images Using Transformer Based Deep Learning Approach

Detection of Abdominal and Mediastinal Lymph Nodes in Comput...

引用

Next Generation Communication & Information Processing (INCIP), International Conference on

作者： Thirandasu Bhargava Giri Parimi Nayeeni Kaushik Reddy Hitesh Tekchandani Madhu Oruganti Praveen Kumar Shukla Pallavi Joshi Artificial Intelligence and Data Science Koneru Lakshmaiah Education Foundation Hyderabad India School of Computing and Intelligent Systems Manipal University Jaipur Jaipur India Department of Computer Science Amrita Vishwa Vidyapeetham Mysuru Mysuru India

ISBN: (数字)9798331528140

ISBN: (纸本)9798331528157

Accurate disease diagnosis and treatment planning for lymph node (LN)-related conditions depend on the precise detection of LN s in radiological modalities, such as computed tomography (CT) images. However, due to the sparse distribution of LN s, their varying sizes, low contrast, and tendency to form clusters, manual detection in CT images becomes challenging. Existing methodologies have not demonstrated sufficiently promising results to be considered as significant solutions. Hence, there is a need for a more reliable method for the automatic detection of LN s in CT images. In this paper, the authors propose a state-of-the-art transformer-based deep learning approach for detecting mediastinal lymph nodes (MLN s) and abdominal lymph nodes (ALN s) in CT scans. To demonstrate the efficacy and usefulness of the proposed methodology, the authors conducted comparative experiments with popular deep learning models such as YOLOv8 and SAM. With the proposed methodology, we achieved recall and precision rates of 98% for ALNs. For MLNs, the recall rate was 99% and the precision rate was 98%. We were able to obtain a 98.73% DS (dice score) for the mediastinal lymph nodes and a 99% DS (dice score) for the abdominal lymph nodes based on the testing images.

关键词： Deep learning Computed tomography Transformers Planning Reliability Medical diagnosis III-V semiconductor materials Lymph nodes Aluminum nitride Testing

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TOWARDS BRIDGING GENERALIZATION AND EXPRESSIVITY OF GRAPH NEURAL NETWORKS

arXiv

引用

arXiv 2024年

作者： Li, Shouheng Geerts, Floris Kim, Dongwoo Wang, Qing School of Computing Australian National University Australia Department of Computer Science University of Antwerp Belgium CSE & GSAI POSTECH Korea Republic of Data61 CSIRO Australia

Expressivity and generalization are two critical aspects of graph neural networks (GNNs). While significant progress has been made in studying the expressivity of GNNs, much less is known about their generalization capabilities, particularly when dealing with the inherent complexity of graph-structured data. In this work, we address the intricate relationship between expressivity and generalization in GNNs. Theoretical studies conjecture a trade-off between the two: highly expressive models risk overfitting, while those focused on generalization may sacrifice expressivity. However, empirical evidence often contradicts this assumption, with expressive GNNs frequently demonstrating strong generalization. We explore this contradiction by introducing a novel framework that connects GNN generalization to the variance in graph structures they can capture. This leads us to propose a k-variance margin-based generalization bound that characterizes the structural properties of graph embeddings in terms of their upper-bounded expressive power. Our analysis does not rely on specific GNN architectures, making it broadly applicable across GNN models. We further uncover a trade-off between intra-class concentration and inter-class separation, both of which are crucial for effective generalization. Through case studies and experiments on real-world datasets, we demonstrate that our theoretical findings align with empirical results, offering a deeper understanding of how expressivity can enhance GNN generalization. © 2024, CC BY.

关键词： Graph neural networks

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Enhancing Stochastic Optimization for Statistical Efficiency Using ROOT-SGD with Diminishing Stepsize

arXiv

引用

arXiv 2024年

作者： Zhang, Tong Li, Chris Junchi Siebel School of Computing and Data Science University of Illinois UrbanaIL United States Department of Electrical Engineering and Computer Sciences University of California BerkeleyCA United States

In this paper, we revisit ROOT-SGD, an innovative method for stochastic optimization to bridge the gap between stochastic optimization and statistical efficiency. The proposed method enhances the performance and reliability of ROOT-SGD by integrating a carefully designed diminishing stepsize strategy. This approach addresses key challenges in optimization, providing robust theoretical guarantees and practical benefits. Our analysis demonstrates that ROOT-SGD with diminishing stepsize achieves optimal convergence rates while maintaining computational efficiency. By dynamically adjusting the learning rate, ROOT-SGD ensures improved stability and precision throughout the optimization process. The findings of this study offer valuable insights for developing advanced optimization algorithms that are both efficient and statistically robust. Copyright © 2024, The Authors. All rights reserved.

关键词： Optimization

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The Prospect of Enhancing Large-Scale Heterogeneous Federated Learning with Foundation Models

The Prospect of Enhancing Large-Scale Heterogeneous Federate...

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IEEE International Conference on Multimedia and Expo (ICME)

作者： Yulan Gao Zhaoxiang Hou Chengyi Yang Zengxiang Li Han Yu Xiaoxiao Li College of Computing and Data Science Nanyang Technological University Singapore Digital Research Institute ENN Group China Department of Electrical and Computer Engineering The University of British Columbia Canada

ISBN: (数字)9798350390155

ISBN: (纸本)9798350390162

Federated learning (FL) addresses data privacy concerns by enabling collaborative training of AI models across distributed data owners. Wide adoption of FL faces the fundamental challenges of data heterogeneity and the large scale of data owners involved. In this paper, we investigate the prospect of Foundation Model (e.g., transformers)-based FL for achieving generalization and personalization in this setting. Different from existing research efforts which mostly focus on studying Transformer-based FL on small scales, we conduct extensive comparative experiments involving FL with Transformers, ResNet, and personalized ResNet-based FL approaches under various large-scale scenarios. These experiments consider varying numbers of data owners to demonstrate Transformers’ advantages over deep neural networks in large-scale heterogeneous FL tasks. In addition, we analyze the superior performance of Transformers by comparing the Centered Kernel Alignment (CKA) representation similarity across different layers and FL models to gain insight into the reasons behind their promising capabilities.

关键词： Training Analytical models data privacy Accuracy Federated learning Distributed databases Transformers

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Super-Resolution of Diffusion-Weighted Images via TDI-Conditioned Diffusion Model 15th

Super-Resolution of Diffusion-Weighted Images via TDI-Cond...

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15th International Workshop on Computational Diffusion MRI, CDMRI 2024, held in conjunction with 27th International Conference on Medical Image computing and Computer-Assisted Intervention, MICCAI 2024

作者： Ma, Jiquan Teng, Yujun Chen, Geng Jiang, Haotian Zhang, Kai Liu, Feihong Rekik, Islem Shen, Dinggang School of Computer Science and Technology Heilongjiang University Harbin China National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology School of Computer Science and Engineering Northwestern Polytechnical University Xi’an China School of Biomedical Engineering and State Key Laboratory of Advanced Medical Materials and Devices ShanghaiTech University Shanghai China School of Information Science and Technology Northwest University Xi’an China BASIRA Lab Imperial-X and Department of Computing Imperial College London London United Kingdom

ISBN: (纸本)9783031869198

Diffusion-Weighted Imaging (DWI) is a significant technique for studying white matter. However, it suffers from low-resolution obstacles in clinical settings. Post-acquisition Super-Resolution (SR) can enhance the resolution of DWIs and has gained increasing research interest in recent years. An advanced generative model, the Diffusion Model (DM), exhibits particularly promising performance in image SR. However, effective conditions are required to bootstrap the DM for DWI SR. To this end, we proposed the first DM-based DWI SR model with two effective conditions based on low-solution DWIs and Track Density Imaging (TDI) maps, which possess rich high-resolution prior knowledge Additionally, we consider another condition based on features from low-resolution DWIs. These two conditions are integrated into our model, which comprises three components: DWI Resolution Enhancer (DRE), DWI Feature Extractor (DFE), and TDI Feature Extractor (TFE). DRE combines low-resolution DWI features from DFE with TDI features from TFE to progressively generate high-resolution DWIs. We performed extensive experiments on DWIs of normal subjects from human connectome projects and patients with Parkinson’s disease. The results demonstrate that our model outperforms existing DWI SR models, both qualitatively and quantitatively. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Neurodegenerative diseases

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