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3rd International Conference on Signal and Data Processing, ICSDP 2023

作者： Jansi, R. Ashwini, K. Aishwarya, N. Muthulakshmi, M. Logeshwari, M. Kanna, Mukesh Department of Electronics and Communication Engineering Faculty of Engineering and Technology SRM Institute of Science and Technology Tamil Nadu Kattankulathur603203 India Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Chennai India Department of Electronics and Communication Engineering Amrita School of Engineering Amrita Vishwa Vidyapeetham Chennai India Department of Mathematics Sathyabama Institute of Science and Technology Semmancheri Tamil Nadu Chennai600119 India

ISBN: (纸本)9789819795772

In recent days, encryption of satellite imagery has attracted a wide range of researchers since these images contain confidential information. Thereby, it is necessary to preserve the privacy of this data to avoid unauthorized access. Two-fish algorithm is one of the widely used image encryption schemes. However, the main drawback of this scheme lies with the static nature of the S-box. To avoid this issue and to improve the quality of encryption, in this paper, we propose a novel algorithm based on chaotic map to create a dynamic S-box structure. In the proposed Chaotic S-Box-based Two-Fish (CSTF) algorithm, a novel chaotic map with improved Lyapunov exponent and approximate entropy has been proposed. The sequences produced by the proposed chaotic map are used to randomize the values inside the static S-box structure of Two-fish algorithm. This encryption scheme is suitable for the encryption of satellite images. The proposed algorithm is validated in terms of S-box evaluation metrics like nonlinearity, bit independence criteria-nonlinearity (BIC-nonlinearity), strict Avalanche criterion (SAC) and bit independence criteria-strict Avalanche criterion (BIC-SAC). To validate the proposed algorithm in terms of key sensitivity, tests like Unified Average Changing Intensity (UACI), Hamming Distance (HD) and Number of Pixels Change Rate (NPCR) have been performed. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Lyapunov functions

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On Efficient Variants of Segment Anything Model: A Survey

arXiv

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arXiv 2024年

作者： Sun, Xiaorui Liu, Jun Shen, Heng Tao Zhu, Xiaofeng Hu, Ping School of Computer Science and Engineering UESTC China School of Computing and Communications Lancaster University United Kingdom School of Computer Science and Technology Tongji University China

The Segment Anything Model (SAM) is a foundational model for image segmentation tasks, known for its strong generalization across diverse applications. However, its impressive performance comes with significant computational and resource demands, making it challenging to deploy in resource-limited environments such as mobile devices. To address this, a variety of SAM variants have been proposed to enhance efficiency without sacrificing accuracy. This survey provides the first comprehensive review of these efficient SAM variants. We begin by exploring the motivations driving this research. We then present core techniques used in SAM and model acceleration. This is followed by an in-depth analysis of various acceleration strategies, categorized by approach. Finally, we offer a unified and extensive evaluation of these methods, assessing their efficiency and accuracy on representative benchmarks, and providing a clear comparison of their overall performance. Copyright © 2024, The Authors. All rights reserved.

关键词： Image segmentation

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Traffic Forecasting & Route Optimization in Smart Environment Using Graph Representation Learning 9

Traffic Forecasting & Route Optimization in Smart Environmen...

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9th IEEE International Smart Cities Conference, ISC2 2023

作者： Shah, Syed Muhammad Ahmed Hassan Shah, Syed Faizan Hussain Hussain, Saddam Turrakheil, Kanishka Sauis Comsats University Islamabad Department of Computer Science Punjab Attock Pakistan School of Digital Science Universiti Brunei Darussalam Brunei Gadong Brunei School of Computing & Engineering University of West London London United Kingdom

ISBN: (纸本)9798350397758

Route optimization and traffic forecasting play pivotal roles in efficient traffic management and the development of intelligent transportation systems in smart cities. In tandem, traffic forecasting offers real-time insights into traffic conditions, empowering proactive decision-making and efficient resource allocation. In this paper, we introduce a novel approach to address the critical challenges of traffic forecasting and route optimization by harnessing the power of Graph Representation Learning (GRL). Utilizing advanced Graph Representation Learning methodologies enables the customization of traffic management systems to accommodate the distinct mobility requirements of individuals with disabilities. This involves optimizing routes that prioritize both efficiency and accessibility, accounting for features like wheelchair accessibility, curb cuts, and pedestrian-friendly routes. The integration of real-time traffic forecasting offers additional empowerment to People with Disabilities (PwD), furnishing timely information about traffic conditions. This assists them in making informed decisions for more seamless and secure commuting encounters. The integration of GRL in traffic management fosters the establishment of sustainable, accessible, and optimized systems, leading to reduced congestion and an elevated commuting experience in smart cities. © 2023 IEEE.

关键词： Decision making

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Optimal Joint Trajectory and Caching with Graph-based Federated Knowledge Distillation in UAV-assisted Networks

Optimal Joint Trajectory and Caching with Graph-based Federa...

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2023 International Conference on Networks, Communications and Intelligent computing, NCIC 2023

作者： Maale, Gerald Tietaa Kuadey, Noble Arden Elorm Sun, Guolin Liu, Guisong School of Computer Science and Engineering UESTC Natl. Key Lab. for Intelligent Collab. Computing Intelligent Term. Key Lab. of Sichuan Prov. Yibin Chengdu China School of Computing and Artificial Intelligence Southwestern University of Finance and Economics Chengdu China

ISBN: (纸本)9798350371949

The use of unmanned aerial vehicles (UAVs) as mobile cache nodes can enhance data traffic throughput and reduce user latency in urban areas. However, jointly optimizing UAV trajectories for efficient content delivery and onboard caching strategies presents significant challenges stemming from the tight coupling between these two problems, as well as issues related to the non-independent and non-identically distributed (non-IID) nature of real-world content popularity and wireless channel dynamics. To address these challenges, we propose KDLMGCN, a deep learning framework that employs graph convolutional networks with LSTM to capture intricate relationships between UAV locations and ground user content demand patterns. Through empirical evaluation, we demonstrate that KDLMGCN surpasses benchmark methods in terms of convergence, cache hit rates, and average transmission, with real-world trajectory dataset and Zipf-based content popularity data. © 2023 IEEE.

关键词： Distillation

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Shield-U: Safeguarding Traffic Sign Recognition Against Perturbation Attacks 23

Shield-U: Safeguarding Traffic Sign Recognition Against Pert...

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23rd IEEE International Conference on Trust, Security and Privacy in computing and Communications, TrustCom 2024

作者： Xu, Shengmin Sun, Jianfei Cao, Hangcheng Gao, Yulan He, Ziyang Wu, Cong Fujian Normal University College of Computer and Cyber Security China Singapore Management University School of Computing and Information Systems Singapore City University of Hong Kong Department of Computer Science Hong Kong Kth Royal Institute of Technology Division of Information Science and Engineering Sweden Zhengzhou University School of Cyber Science and Engineering China Nanyang Technological University School of Computer Science and Engineering Singapore

Traffic sign recognition systems are crucial for the navigation and situation awareness of autonomous vehicles. They leverage deep learning technologies to swiftly and accurately identify traffic signs, even in the most challenging traffic environments. However, security researchers have uncovered a critical vulnerability in these systems: learning-based TSRs are particularly susceptible to physical-world perturbation attacks. Through subtle modifications (i.e., attaching well-designed patches on traffic signs), attackers can deceive the recognition system into making erroneous judgments, which can further lead to serious traffic accidents. Although several defense mechanisms have been proposed to enhance the security of sign recognition systems, these solutions generally target only specific types of malicious perturbations and thus lack robustness. To address this issue, we present a robust defense mechanism named Shield-U, which restores traffic sign images contaminated by physical patch perturbations, providing credible data for the recognition model. In the process of implementing Shield-U, we first design a feature difference-aware perturbation generator that outputs potential sign contamination patterns. Incorporating generated perturbations during the training phase enables our restoration model to gain sufficient understanding of diverse perturbation types, thus enhancing its ability to repair various perturbed signs. Following this, we build an attention-driven restoration network to repair sign images. Finally, we evaluate the effectiveness of Shield-U using widely used sign recognition models and public datasets. The results demonstrate that our defense mechanism excels in resisting potential perturbations, increasing the average sign recognition accuracy by 50.4%. © 2024 IEEE.

关键词： defense mechanism perturbation attack public transportation safety Traffic sign recognition

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AI-Powered Symptom Checker Chatbot in Regional Languages

AI-Powered Symptom Checker Chatbot in Regional Languages

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Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI), IEEE Conference on

作者： R. Henry Koushal C. Venkata Sai Pranav Raushan Kumar Gupta K. Hemesh Verma Nandu C. Nair Computer Science and Engineering Amrita Vishwa Vidyapeetham Bengaluru India Department of Computer Science and Engineering Amrita School of Computing Bengaluru Amrita Vishwa Vidyapeetham India

ISBN: (数字)9798331521691

ISBN: (纸本)9798331521707

This paper further encapsulates an AI-based symptom checker in the form of an automated chatbot to provide health information and advice in local languages to help the multitudes of non-English speakers find the medical assistance they need. The chatbot allows the user to provide symptoms’ description in natural Language, eradicating language barrier as a cause of inadequate healthcare. Due to the application of NLP algorithms, the system provides accurate symptom description analysis while also providing an easily comprehensible experience for the user. When symptoms are assessed, the chatbot provides initial diagnoses of possible diseases or illnesses, as well as advice on what steps to take if the symptoms worsen or if the user should seek professional medical attention. They get the information when they need it most therefore improving early symptoms control and encouraging preventive health care. Thus, making language inclusivity, the system helps to make healthcare guidance more accessible to underserved communities.

关键词： Technological innovation Accuracy Medical services Linguistics Chatbots Market research Reliability Data mining Medical diagnostic imaging Diseases

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Topic identification of text-based expert stock comments using multi-level information fusion

Topic identification of text-based expert stock comments usi...

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作者： Zhao, Feng Zhang, Jiahui Chen, Zhiyuan Zhang, Xiaofeng Xie, Qingsong School of Computer Science and Technology Shandong Technology and Business University Yantai China Shandong Co-Innovation Center of Future Intelligent Computing Yantai China School of Information and Electrical Engineering Ludong University Yantai China

Stock investment is an important mode of asset allocation and a crucial means of financial management. How to grasp the movement of stock price and predict its trend have been the focus of investors and investment companies. Since expert stock comments contain abundant essential information for investment decisions, how to identify the topic of expert stock comments with high precision and efficiency is an important research topic. However, the existing methods usually employ single feature selection strategies for topic identification of stock comments, which may lead to low accuracy. Thus, to deal with this limitation, we propose a multi-level information fusion method to construct a topic identification system of stock comments. Specifically, we firstly fuse various complementary feature selection methods via a multi-view learning framework which can comprehensively represent text-based topics. In addition, regarding the decision process, we propose a fusion strategy based on belief value which can further improve the classification performance. The experimental results indicate that the proposed multi-level information fusion method is not only superior to other methods in terms of classification, it is also able to accurately capture topics of expert stock comments. © 2020 John Wiley & Sons Ltd.

关键词： Information fusion

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U-Net-Attention-TBNet: A Cutting-Edge Solution for Accurate TB Lesion Segmentation and Classification 8

U-Net-Attention-TBNet: A Cutting-Edge Solution for Accurate ...

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8th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2024

作者： Karthikeyan, M. Saravana Raj, J. Relin Francis Parvathi, R. Anushya, S. Thanga Krishnan, R. Santhana Joshua, K. Paul Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Chennai Avadi India SaveethaInstitute of Medical and Technical Sciences Saveetha School of Engineering Department of Electronics and Communication Engineering Chennai India Department of Smart Computing Vellore India Scad College of Engineering and Technology Department of Computer Science and Engineering Cheranmahadevi India Scad College of Engineering and Technology Department of Electronics and Communication Engineering Cheranmahadevi India

ISBN: (纸本)9798350376425

U-Net-Attention-TBNet is an innovative deep-learning model developed for precisely segmenting and classifying tuberculosis (TB) lesions in chest X-ray images. This Model combines the U-Net architecture with an advanced attention mechanism, enhancing feature extraction and boosting detection accuracy. Trained on the CheXpert dataset, U-Net-Attention-TBNet demonstrates superior performance compared to existing models, including DenseNet with U-Net, ResNet with U-Net, and VGGNet with U-Net. It achieves significantly higher accuracy, precision, recall, and F1 score, showcasing its effectiveness in distinguishing between primary TB lesions, secondary TB lesions, miliary TB lesions, and calcified granulomas. The attention mechanism refines the Model's ability to focus on pertinent features, leading to improved segmentation and reduced false positives. This progress represents a significant advancement in TB diagnosis, offering enhanced reliability and efficiency in medical imaging and contributing to better management of tuberculosis. © 2024 IEEE.

关键词： Medical imaging

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Bio-Inspired Optimization with Transfer Learning Based Crowd Density Detection on Sparse Environment 3

Bio-Inspired Optimization with Transfer Learning Based Crowd...

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3rd International Conference on Pervasive computing and Social Networking, ICPCSN 2023

作者： Poongodi, A. Kopperundevi, N. Poorani, S. Tiwari, Mohit Rao, B. Srinivasa Abilash, N. School of Computing Sciences Vels Institute of Science Technology & Advanced Studies Chennai India School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Vellore India Computer Technology-UG Kongu Engineering College Perundurai India Bharati Vidyapeeth's College of Engineering Computer Science and Engineering A-4 Rohtak Road Paschim Vihar Delhi110063 India Gokaraju Rangaraju Institute of Engineering & Technology Computer Science and Engineering Hyderabad Bachupally500090 India Annai Vailankanni College of Engineering Mechanical Engineering Kanyakumari District Pottalkulam India

ISBN: (纸本)9798350322842

Crowd density estimation is a major importance for applications including crowd control, public space planning, autonomous driving, visual surveillance, and warning visually distrait drivers previous accident. With strong scale, reflective, and translational symmetry, techniques to estimate the density of the crowd yield a promising outcome. But dynamic scenes with constantly evolving spatial and temporal domains and perspective distortion yet have difficulties. The dynamic nature of scenes and the complexity of demonstrating and integrating the feature space of objects of different magnitudes as predictive prototypes are the primary reason for this. This manuscript presents a Red Fox Algorithm with Transfer Learning based Crowd Density Detection (RFOTL-CDD) technique in Sparse Environment. The purpose of the RFOTL-CDD system lies in the effectual identification and classification of distinct types of crowds in a sparse environment. To achieve this, the presented RFOTL-CDD method uses a ResNet prototype for feature vector generation. For the identification and classification of a crowd, the RFOTL-CDD technique applies Naive Bayes (NB) classifiers. In this work, the RFO algorithm is utilized for boosting the performance of the ResNet method. The stimulation outcomes of the RFOTL-CDD technique can be well studied on a crowd dataset and the outcomes confirmed the supremacy of the RFOTL-CDD technique on crowd detection. © 2023 IEEE.

关键词： Deep learning

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Hotpatching on the Fly - Mitigating Drone Incidents Arising from Incorrect Configuration

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IEEE Transactions on Dependable and Secure computing 2025年

作者： Han, Ruidong Li, Juanru Ma, Zhuo Lo, David Shaghaghi, Arash Ma, JianFeng Ma, Siqi Xidian University School of Cyber Engineering Xi'an China Singapore Management University School of Computing and Information Systems Singapore Feiyu Security Shanghai China Unsw Sydney School of Computer Science and Engineering Australia

Manufacturers offer adjustable control parameters for flight control systems to accommodate diverse environments and missions. To ensure flight safety, they also develop established boundaries, i.e., range specifications for parameter values. However, even when the configuration parameters fall within the prescribed manufacturer range, they could still lead to instability or even severe incidents like crashes, which are referred to as Range Specification Bugs. Prior research has suggested shrinking the range of parameter values to protect drones from the adverse effects of such bugs. However, narrowing the range of parameters may only reduce the probability of errors and could potentially limit the drone's adaptability. To overcome this limitation, we present an online approach that analyzes a sequence of flight states to detect any potential triggering of bugs and rectify the drone by dynamically adjusting its parameters. We implemented the rectification approach, ConFix, and applied it in current prevalent flight control systems, Ardupilot and PX4. The results demonstrated that ConFix achieved an average rectification success at 80% on average. © 2004-2012 IEEE.

关键词： Specifications

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