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AI-based traffic analysis in digital twin networks

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arXiv 2024年

作者： Al-Shareeda, Sarah Huseynov, Khayal Cakir, Lal Verda Thomson, Craig Ozdem, Mehmet Canberk, Berk Artificial Intelligence and Data Engineering Department Istanbul Technical University Turkey BTS Group Istanbul Turkey School of Computing Engineering and The Built Environment Edinburgh Napier University United Kingdom Turk Telekom Istanbul Turkey

In today’s networked world, Digital Twin Networks (DTNs) are revolutionizing how we understand and optimize physical networks. These networks, also known as’Digital Twin Networks (DTNs)’ or’Networks Digital Twins (NDTs),’ encompass many physical networks, from cellular and wireless to optical and satellite. They leverage computational power and AI capabilities to provide virtual representations, leading to highly refined recommendations for real-world network challenges. Within DTNs, tasks include network performance enhancement, latency optimization, energy efficiency, and more. To achieve these goals, DTNs utilize AI tools such as Machine Learning (ML), Deep Learning (DL), Reinforcement Learning (RL), Federated Learning (FL), and graph-based approaches. However, data quality, scalability, interpretability, and security challenges necessitate strategies prioritizing transparency, fairness, privacy, and accountability. This chapter delves into the world of AI-driven traffic analysis within DTNs. It explores DTNs’ development efforts, tasks, AI models, and challenges while offering insights into how AI can enhance these dynamic networks. Through this journey, readers will gain a deeper understanding of the pivotal role AI plays in the ever-evolving landscape of networked systems. © 2024, CC BY.

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GoMatching: A Simple Baseline for Video Text Spotting via Long and Short Term Matching

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arXiv 2024年

作者： He, Haibin Ye, Maoyuan Zhang, Jing Liu, Juhua Du, Bo Tao, Dacheng School of Computer Science National Engineering Research Center for Multimedia Software Institute of Artificial Intelligence Wuhan University China College of Computing & Data Science Nanyang Technological University Singapore

Beyond the text detection and recognition tasks in image text spotting, video text spotting presents an augmented challenge with the inclusion of tracking. While advanced end-to-end trainable methods have shown commendable performance, the pursuit of multi-task optimization may pose the risk of producing sub-optimal outcomes for individual tasks. In this paper, we identify a main bottleneck in the state-of-the-art video text spotter: the limited recognition capability. In response to this issue, we propose to efficiently turn an off-the-shelf query-based image text spotter into a specialist on video and present a simple baseline termed GoMatching, which focuses the training efforts on tracking while maintaining strong recognition performance. To adapt the image text spotter to video datasets, we add a rescoring head to rescore each detected instance's confidence via efficient tuning, leading to a better tracking candidate pool. Additionally, we design a long-short term matching module, termed LST-Matcher, to enhance the spotter's tracking capability by integrating both long- and short-term matching results via Transformer. Based on the above simple designs, GoMatching delivers new records on ICDAR15-video, DSText, BOVText, and our proposed novel test with arbitrary-shaped text termed ArTVideo, which demonstrates GoMatching's capability to accommodate general, dense, small, arbitrary-shaped, Chinese and English text scenarios while saving considerable training budgets. Copyright © 2024, The Authors. All rights reserved.

关键词： Budget control

AgrInnovate – Prediction of Crop Yields and Fertilizer Recommendations using IOT and Random Forest Decision Tree

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AgrInnovate – Prediction of Crop Yields and Fertilizer Reco...

Communication, computing and Energy Efficient Technologies (I3CEET), International Conference on

作者： M.Thamizharasi A. Sandhya G. Deena R. Sethuraman Department of Artificial Intelligence and Data Science Rajalakshmi Engineering College Chennai Department of CSE SRM Institute of Science and Technology Chennai School of Computing Sathyabama Institute of Science and Technology Chennai

ISBN: (数字)9798331541583

ISBN: (纸本)9798331541590

AgrInnovate presents a pioneering solution in precision agriculture, applying cutting-edge machine learning techniques to provide accurate fertilizer and crop prediction. Leveraging data from soil sensors capturing critical parameters like pH, temperature, and NPK values, the system offers tailored guidance for crop selection and fertilizer application. Employing a Random Forest Classifier, the model achieves exceptional accuracy, with crop prediction with accuracy of 98.7% and fertilizer recommendation reaching 97.9%. Integration with a user-friendly website enhances accessibility, allowing stakeholders to access and interpret predictions seamlessly. AgrInnovate is a noteworthy technological development in agriculture that offers a comprehensive solution to optimize farming practices and increase productivity.

关键词： Temperature sensors Temperature distribution Accuracy Soil Sensor systems and applications Telecommunication computing Stakeholders Random forests Fertilizers Farming

Correction to: Advanced model based machine learning technique for early stage prediction of ankylosing spondylitis under timely analysis with featured textures

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Multimedia Tools and Applications 2024年第26期83卷 68415-68420页

作者： Ahammad, Shaik Hasane Jayaraj, R. Shibu, S. Sujatha, V. Prathima, Ch Leo, L. Megalan Prabu, R. Thandaiah Hossain, Md.Amzad Rashed, Ahmed Nabih Zaki Department of ECE Koneru Lakshmaiah Education Foundation Vaddeswaram India Department of Data Science and Buisness Systems School of Computing SRM Institute of Science and Technology Kattankulathur Chennai India Department of Electronics and Communication Engineering Panimalar Engineering College Chennai India Department of Master of Computer Applications S.A. Engineering College Chennai India Department of Data Science School of Computing Mohan Babu University Erstwhile Sree Vidyanikethan Engineering College) Tirupati India Deptartment of Electronics and Communication Engineering Sathyabama Institute of Science and Technology Chennai India Department of ECE Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences SIMATS Saveetha University Chennai India Department of Electrical and Electronic Engineering Jashore University of Science and Technology Jashore Bangladesh Electronics and Electrical Communications Engineering Department Faculty of Electronic Engineering Menoufia University Menouf Egypt

OblivTime: Oblivious and Efficient Interval Skyline Query Processing Over Encrypted Time-Series data

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IEEE Transactions on Services computing 2025年第03期18卷 1602-1617页

作者： Ouyang, Huajie Zheng, Yifeng Wang, Songlei Hua, Zhongyun Harbin Institute of Technology School of Computer Science and Technology Guangdong Shenzhen518055 China The Hong Kong Polytechnic University Department of Electrical and Electronic Engineering Hong Kong Shenzhen University National Engineering Laboratory for Big Data System Computing Technology Shenzhen518055 China

Time-series data is prevalent in many applications like smart homes, smart grids, and healthcare. And it is now increasingly common to store and query time-series data in the cloud. Despite the benefits, data privacy concerns in such outsourced services are pressing, making it imperative to embed privacy assurance mechanisms from the outset. Most existing related works have been focused on querying for different types of aggregate statistics. In this paper, we instead focus on the secure support for advanced interval skyline queries, which allow to identify time series that are not dominated by any other time series within a query time interval. This is valuable for time-series data analytics in applications like remote health monitoring (e.g., identifying patients with high heart rates in a certain week). We present OblivTime, a new system framework for oblivious and efficient interval skyline query processing over encrypted time-series data. OblivTime is built from a synergy of time-series data analytics, lightweight cryptography, and GPU parallel computing, achieving stronger security guarantees and lower online query latency over the state-of-the-art prior work. Extensive experiments demonstrate that OblivTime can achieve up to 666\times speedup in online query latency over the state-of-the-art prior work. © 2008-2012 IEEE.

关键词： Structured Query Language

Multi-Head Self-Attention Based Arabic News Recommendation System

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Multi-Head Self-Attention Based Arabic News Recommendation S...

International Conference on Advanced Aspects of Software engineering (ICAASE)

作者： Karboua Sabrina Fouzi Harrag Farid Meziane Departement of Computer Science Mechatronics Laboratory Ferhat ABBAS University Setif 1 Setif Algeria Data Science Research Centre School of Computing and Engineering University of Derby United Kingdom

ISBN: (数字)9798331522810

ISBN: (纸本)9798331522827

This paper introduces an Arabic News Recommendation System (ANRS) designed to enhance personalized news recommendations for Arabic-speaking users. By leveraging a multi-head self-attention mechanism, the proposed model effectively captures complex dependencies within Arabic text, addressing challenges posed by the language's rich morphology and contextual nuances. The news encoder uses multi-head self-attention to learn the meaning of Arabic news articles by analyzing interactions between words in titles, body text. Meanwhile, the user encoder models user preferences by examining previously read articles, also using multi-head self-attention to capture connections between articles. Trained on a dataset of Arabic news articles and 15 million user interaction logs, ANRS achieves a high prediction accuracy of 98.6%, significantly outperforming traditional and deep learning-based Arabic recommendation models.

关键词： Translation Semantics Morphology Predictive models Encoding Multilingual Internet Recommender systems Software engineering Context modeling

3DMambaIPF: A State Space Model for Iterative Point Cloud Filtering via Differentiable Rendering

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arXiv 2024年

作者： Zhou, Qingyuan Yang, Weidong Fei, Ben Xu, Jingyi Zhang, Rui Liu, Keyi Luo, Yeqi He, Ying School of Computer Science Fudan University China Department of Information Engineering The Chinese University of Hong Kong Hong Kong College of Computing and Data Science Nanyang Technological University Singapore

Noise is an inevitable aspect of point cloud acquisition, necessitating filtering as a fundamental task within the realm of 3D vision. Existing learning-based filtering methods have shown promising capabilities on commonly used datasets. Nonetheless, the effectiveness of these methods is constrained when dealing with a substantial quantity of point clouds. This limitation primarily stems from their limited denoising capabilities for dense and large-scale point clouds and their inclination to generate noisy outliers after denoising. To deal with this challenge, we introduce 3DMambaIPF, for the first time, exploiting Selective State Space Models (SSMs) architecture to handle highly-dense and large-scale point clouds, capitalizing on its strengths in selective input processing and large context modeling capabilities. Additionally, we present a robust and fast differentiable rendering loss to constrain the noisy points around the surface. In contrast to previous methodologies, this differentiable rendering loss enhances the visual realism of denoised geometric structures and aligns point cloud boundaries more closely with those observed in real-world objects. Extensive evaluations on commonly used datasets (typically with up to 50K points) demonstrate that 3DMambaIPF achieves state-of-the-art results. Moreover, we showcase the superior scalability and efficiency of 3DMam-baIPF on highly dense and large-scale point clouds with up to 500K points compared to off-the-shelf methods. Copyright © 2024, The Authors. All rights reserved.

关键词： Wiener filtering

Facial Emotion Recognition using YOLO based Deep Learning Classifier 1

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Facial Emotion Recognition using YOLO based Deep Learning Cl...

1st International Conference on Electronics, Communication and Signal Processing, ICECSP 2024

作者： Vanamoju, Sri Vaibhav Mohan Dev Vineetha, Moturi Veda Tekchandani, Hitesh Joshi, Pallavi Shukla, Praveen Kumar Khanna, Anita Electronics and Communication Koneru Lakshmaiah Education Foundation Hyderabad India Artificial Intelligence and Data Science Koneru Lakshmaiah Education Foundation Hyderabad India Amrita Vishwa Vidyapeetham Mysuru Department of Computer Science Mysuru India School of Computing and Intelligent Systems Manipal University Jaipur Jaipur India Electrical Engineering Guru Ghasidas Vishwavidyalaya Bilaspur Bilaspur India

ISBN: (纸本)9798350364590

Understanding facial expressions is key to successful communication. It's like decoding a secret language that helps us know if someone is happy, sad, angry, surprised, or something else altogether. Harnessing facial emotion recognition (FER) in machines holds the potential to revolutionize human-computer interaction, improve customer service experiences and provide invaluable support in medical diagnosis. Machines can comprehend human's needs more effectively by reading the emotions from their facial expression and therefore this results in a more natural conversation between them and the machine. Various machine and deep learning methodologies have been implemented for FER. In this research work, the authors propose the state-of-the-art YOLO (You Only Look Once) based deep learning classifier for FER. The YOLO algorithm is a single-shot approach that efficiently classifies objects in a single pass. It achieves this by using a single neural network to predict bounding boxes and target class probabilities by utilizing the entire image as input. YOLOv8 is a recent iteration of the YOLO object detection algorithm is popular for its speed and accuracy which offers improvements over prior versions, making it a suitable choice for real-time intended task of FER. With the proposed classifier, the authors achieved test accuracy of 72.56% on the FER-2013 dataset in a seven-class FER task. © 2024 IEEE.

关键词： Adversarial machine learning

Gender Bias in Text-to-Video Generation Models: A case study of Sora

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arXiv 2024年

作者： Nadeem, Mohammad Sohail, Shahab Saquib Cambria, Erik Schuller, Björn W. Hussain, Amir Department of Computer Science Aligarh Muslim University Aligarh India School of Computing Science and Engineering VIT Bhopal University MP Sehore466114 India College of Computing and Data Science Nanyang Technological University Singapore Technical University of Munich Germany Imperial College London United Kingdom School of Computing Edinburgh Napier University United Kingdom

The advent of text-to-video generation models has revolutionized content creation as it produces high-quality videos from textual prompts. However, concerns regarding inherent biases in such models have prompted scrutiny, particularly regarding gender representation. Our study investigates the presence of gender bias in OpenAI's Sora, a state-of-the-art text-to-video generation model. We uncover significant evidence of bias by analyzing the generated videos from a diverse set of gender-neutral and stereotypical prompts. The results indicate that Sora disproportionately associates specific genders with stereotypical behaviors and professions, which reflects societal prejudices embedded in its training data. Copyright © 2024, The Authors. All rights reserved.

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