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15th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2024

作者： Kavitha, K. Gopalakrishnan, K. Balaji, S. Jeevanantham, J. Aakhila Hayathunisa, M. Department of Information Technology Velalar College of Engineering Erode India Department of Computer Science and Engineering Kongu Engineering College Erode India Department of Electronics and Communication Kongu Engineering College Erode India

ISBN: (纸本)9798331300579

Deep learning is a powerful machine learning technique that has the potential to revolutionize agriculture. By leveraging deep learning models, we can develop innovative solutions to critical agricultural challenges, such as crop classification, weed percentage estimation, and crop quality identification. Deep learning models can be trained on remote sensing data to accurately classify different crop types. This information can be used by farmers and agricultural stakeholders to monitor and manage their crops more effectively. For example, crop classification models can be used to identify areas of a field that are underperforming or at risk of disease. Weed infestations can significantly reduce crop yields and quality. Deep learning models can be trained on high-resolution imagery and sensor data to accurately estimate weed percentages within fields. This information can be used to develop precise and timely weed management strategies, reducing the need for excessive herbicide usage and minimizing the environmental impact. Assessing crop quality is crucial for optimizing harvest decisions and ensuring the delivery of high-quality produce to consumers. Deep learning techniques, such as image analysis and spectroscopy, can be employed to identify various quality parameters in crops, such as size, color, ripeness, and the presence of diseases or blemishes. This information can help farmers make informed decisions about harvesting, storage, and distribution. Assessing crop quality is crucial for optimizing harvest decisions and ensuring the delivery of high-quality produce to consumers. Deep learning techniques, such as image analysis and spectroscopy, can be employed to identify various quality parameters in crops, such as size, color, ripeness, and the presence of diseases or blemishes. The continued development and integration of deep learning technologies into agriculture hold promise for advancing global food production and agricultural sustainability. © Grenze Sc

关键词： Weed control

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Efficient Lightweight Intrusion Detection Method for IoT OS: A Study on the Application of Multi-Teacher Distillation 8

Efficient Lightweight Intrusion Detection Method for IoT OS:...

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8th International Conference on Data science in Cyberspace, DSC 2023

作者： Zhang, Pengyu Xiao, Kun Luo, Lei Li, Yun Chen, Lirong School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China Guangdong Weichen Information Technology Co. Ltd Dongguan China

ISBN: (纸本)9798350331035

As the second important security measure behind the firewall, network intrusion detection provides effective assurance for computer network security. In recent years, deep learning (DL) has emerged as a promising approach for enhancing intrusion detection and achieving high experimental accuracy. However, due to performance limitations caused by resource consumption, deep neural network models with high requirements for floating-point calculations cannot be directly applied to Internet of Things devices. In our study, the decision tree is initially employed to reduce the feature dimension and extract spatial features. We then combine the teacher network based on the Bi-GRU algorithm to capture the temporal traffic features, achieving an accuracy rate of 99.41% on the CICIDS2017 dataset. We use these two high-accuracy models as teachers for multi-teacher knowledge distillation. The student model adopts a lightweight network architecture, with a scale only 2.5% of that of the teacher model. Finally, we compare the performance of the teacher network and the student network on Internet of Things devices. We find that the accuracy loss is only 0.09% compared to the teacher network. Moreover, the accuracy for DDoS attacks has been improved by 30.1% compared to the non-distilled model. The IDS framework we designed is well-suited for scenarios such as the Internet of Vehicles and can be integrated with the cloud to update the device-side model and enhance system security. © 2023 IEEE.

关键词： Bi-GRU Intrusion detection Knowledge distillation Lightweight

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Open-Source Simulators for Drone-Assisted Vehicular Ad Hoc Networks

Open-Source Simulators for Drone-Assisted Vehicular Ad Hoc N...

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International Conference on Data Analytics and Management, ICDAM 2022

作者： Kumar, Santosh Vasudeva, Amol Sood, Manu Department of Computer Science Himachal Pradesh University Shimla India Department of Computer Science and Engineering Jaypee University of Information Technology Himachal Pradesh Solan Waknaghat India

ISBN: (纸本)9789811976148

Intelligent transportation systems (ITS) are a collection of cutting-edge technologies that are intended to offer a variety of road traffic management and safety services. The number of automobiles on the road and flying in three-dimensional (3D) space has increased dramatically, increasing the risk of accidents and other security issues. A new technology based on the network composed of flying drones, mobile vehicles, and ad hoc infrastructure known as UAV-assisted vehicular ad hoc network (VANET), a subtype of mobile ad hoc network (MANET), is designed to deal with security issues observed in VANETs. The applications or protocols designed for vehicular safety in such a network should be thoroughly tested before deployment. In many scenarios, simulations with the help of readily available tools play a significant role within the limited scope of testing in research at various levels. Therefore, such simulation tools and their appropriate selection for specific purposes are essential for validating any research concept before being implemented in various real-world network scenarios. This paper explores available open-source simulators that can be used to simulate UAV-assisted VANETs. Additionally, a comparison based on various features of simulators has also been presented aimed at helping in the selection of a particular tool for specific problems. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Vehicular ad hoc networks

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Data Reduction Algorithm: Identification and Elimination of Erroneous rows

Data Reduction Algorithm: Identification and Elimination of ...

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2023 IEEE International Conference on Research Methodologies in Knowledge Management, Artificial Intelligence and Telecommunication engineering, RMKMATE 2023

作者： Katale, Tejaswini Mahalle, Parikshit N. Gautam, Waidehi Shinde, Gitanjali R. Computer Engineering Vishwakarma Institute of Technology Pune India Artificial Intelligence and Data Science Vishwakarma Institute of Information Technology Pune India

ISBN: (纸本)9798350305708

Data Reduction without the removal of exact, correct rows is a crucial pre-processing step. Large Datasets make it difficult to model data effectively or forecast results accurately. Additionally, they demand lengthy processing times, sophisticated complexity software and thorough data cleaning. The incorrect and irrelevant rows may produce inaccurate results that impair the performance of the model. For better and more accurate outcomes it is crucial to properly detect and remove inaccurate data. The proposed algorithm calculates the Initial Recall value of the Dataset. It eliminates least correlated features using the Correlation Matrix. Using Gaussian Curve, for all the columns it identifies and eliminates rows having values which lie beyond (µ ± 3σ). Furthermore, it takes into account the column with the highest Standard Deviation, selects the nearest 50% Left and 50% Right values from that column's mean. It selects only those rows and calculates the Final Recall value. Negligible difference between Initial and Final Recall values implies that the removed rows had no or minimal impact on the Dataset's final result. This algorithm is implemented on 3 Standard Medical Datasets - Pima Diabetes, Heart Attack and Breast Cancer. For the Breast Cancer Dataset, this algorithm eliminated the highest number of rows that is 231. © 2023 IEEE.

关键词： Correlation Matrix Data Reduction Gaussian Curve Recall Standard Deviation

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Secured Authentication Scheme for E-Healthcare in SIoT Using ECC and Improved Hash Function 4th

Secured Authentication Scheme for E-Healthcare in SIoT Using...

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4th International Conference on Frontiers in Computing and Systems, COMSYS 2023

作者： Arpitha, T. Chouhan, Dharamendra Shreyas, J. Department of Computer Science and Engineering University Visvesvaraya College of Engineering Bangalore University Bengaluru India Department of Information Technology Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Manipal India

ISBN: (纸本)9789819726134

The convergence of Smart Internet of Things (IoT) and security is a new study topic. In this context, a new IoT trend known as Social IoT (SIoT) was examined for this study. The Social IoT has the advantage of connectivity demonstrated within the network of connected things via the IoT. In Social IoT, a wide range of IoT devices, such as wearables or monitoring sensors, are utilized in e-healthcare applications and the risk of identity theft and impersonation is a significant concern. To address this challenge, we propose a scheme that incorporates multi-factor authentication, elliptic curve cryptography (ECC), and hash functions scheme to provide a robust solution for secure and privacy-preserving authentication. Our system provides privacy protection along with mutual authentication and session key negotiation at a considerably low computational cost (of up to 71.04% compared to other existing techniques). © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Internet of things

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A Blockchain-empowered Federated Learning Framework Supprting GDPR-compliance 10

A Blockchain-empowered Federated Learning Framework Supprtin...

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10th IEEE International Conference on Cyber Security and Cloud Computing and 9th IEEE International Conference on Edge Computing and Scalable Cloud, CSCloud-EdgeCom 2023

作者： Xiao, Lijun Han, Dezhi Zhou, Sisi Xu, Nengxiang Chen, Lin Xie, Siqi College of Information Engineering Shanghai Maritime University Shanghai200135 China School of Computer Science and Engineering Hunan University of Science and Technology Xiangtan411201 China

ISBN: (纸本)9798350312461

In recent years, data privacy security has been widely and highly valued by countries around the world. In the context of European Union's General Data Protection Regulation (GDPR), the regulatory requirements of laws and regulations are becoming increasingly strict, bringing huge impacts and challenges to enterprises with user's personal data such as internet services and financial technology. Up to a point, federal learning ensures data privacy by storing and processing personal data locally. However, due to malicious clients or central servers being able to launch attacks on global models or user privacy data, the security of federated learning is questioned, introducing blockchain into the federated learning framework is a feasible solution to address these data security issues. In this work, the concept of Blockchain (BC), Federated Learning (FL), GDPR and other similar data protection laws are presented, where a Blockchain-empowered Federated Learning (BC-empowered FL) framework is introduced. The challenges on complying with the GDPR are described, and the solutions or principles for improving the GDPR-compliance of BC-empowered FL systems are analyzed, sorting out the differences and connections among the GDPR-compliance methods yet laying a foundation to design legal and compliant applications for different domains and scenarios which need touch upon the user's personal data. © 2023 IEEE.

关键词： Data privacy

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GL-GAN: Perceiving and Integrating Global and Local Styles for Handwritten Text Generation with Mamba 31

GL-GAN: Perceiving and Integrating Global and Local Styles f...

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31st International Conference on Computational Linguistics, COLING 2025

作者： Wang, Yiming Wei, Hongxi Wang, Heng Sun, Shiwen He, Chao School of Computer Science Inner Mongolia University Hohhot China National and Local Joint Engineering Research Center of Mongolian Information Processing Technology Hohhot China

ISBN: (纸本)9798891761964

Handwritten text generation (HTG) aims to synthesize handwritten samples by imitating a specific writer, which has a wide range of applications and thus has significant research value. However, current studies on HTG are confronted with a main bottleneck: dominant models lack the ability to perceive and integrate handwriting styles, which affects the realism of the synthesized samples. In this paper, we propose GL-GAN, which effectively captures and integrates global and local styles. Specifically, we propose a Hybrid Style Encoder (HSE) that combines a state space model (SSM) and convolution to capture multilevel style features through various receptive fields. The captured style features are then fed to the proposed Dynamic Feature Enhancement Module (DFEM), which integrates these features by adaptively modeling the entangled relationships between multilevel styles and removing redundant details. Extensive experiments on two widely used handwriting datasets demonstrate that our GL-GAN is an effective HTG model and outperforms state-of-the-art models remarkably. Our code is publicly available at: https://***/Fyzjym/GL-GAN. © 2025 Association for Computational Linguistics.

关键词： Computational linguistics

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Comments on 'V2G-Auth: Lightweight Authentication and Key Agreement Protocol for V2G Environment Leveraging Physically Unclonable Functions'

IEEE Transactions on Industrial Cyber-Physical Systems

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IEEE Transactions on Industrial Cyber-Physical Systems 2024年 2卷 69-70页

作者： Wu, Tsu-Yang Wu, Haozhi Kong, Fangfang Kumari, Saru Khan, Muhammad Khurram Chen, Chien-Ming Shandong University of Science and Technology College of Computer Science and Engineering Qingdao266590 China Nanjing University of Information Science and Technology School of Artificial Intelligence School of Future Technology Nanjing210044 China Chaudhary Charan Singh University Department of Mathematics Meerut250004 India Riyadh11495 Saudi Arabia

Recently, Reddy et al. (IEEE Transactions on Industrial Cyber-Physical Systems, 2023, DOI: 10.1109/TICPS.2023. 3290539) proposed a lightweight authentication and key agreement protocol for V2G environments that uses hash functions and multiple physical unclonable functions. However, we find that Reddy et al.'s protocol cannot resist known session-specific temporary information (KSSTI) attacks. To address these limitations, we discuss an improvement that addresses the above issues. © 2023 IEEE.

关键词： Electronic mail

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D2R: Dual-Branch Dynamic Routing Network for Multimodal Sentiment Detection

D2R: Dual-Branch Dynamic Routing Network for Multimodal Sent...

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2024 Conference on Empirical Methods in Natural Language Processing, EMNLP 2024

作者： Chen, Yifan Li, Kuntao Mai, Weixing Wu, Qiaofeng Xue, Yun Li, Fenghuan Guangdong Provincial Key Laboratory of Quantum Engineering and Quantum Materials School of Electronics and Information Engineering South China Normal University China School of Computer Science and Technology Guangdong University of Technology China

ISBN: (纸本)9798891761643

Multimodal sentiment detection aims to classify the sentiment polarity of a given image-text pair. Existing approaches apply the same fixed framework to all input samples, lacking the flexibility to adapt to different image-text pairs. Furthermore, the interaction patterns of these methods are overly homogenized, limiting the model's capacity to extract multimodal sentiment information effectively. In this paper, we develop a Dual-Branch Dynamic Routing Network (D2R), which is the first multimodal dynamic interaction model towards multimodal sentiment detection. Specifically, we design six independent units to simulate inter- and intra-modal information interactions without depending on any existing fixed frameworks. Additionally, we configure a soft router in each unit to guide path generation and introduce the path regularization term to optimize these inference paths. Comprehensive experiments on three publicly available datasets demonstrate the superiority of our proposed model over state-ofthe-art methods. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

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New Optimization Method Based on Binary Tumbleweed Algorithm 1

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18th International Conference on Intelligent information Hiding and Multimedia Signal Processing, IIH-MSP 2022

作者： Pan, Jeng-Shyang Wang, Gui-Ling Yang, Dongqiang Chu, Shu-Chuan College of Computer Science and Engineering Shandong University of Science and Technology Qingdao266590 China School of Computer Science and Technology Shandong Jianzhu University Jinan China Department of Information Management Chaoyang University of Technology Taichung41349 Taiwan

ISBN: (数字)9789819906055

ISBN: (纸本)9789819906048

Tumbleweed algorithm (TA) is a meta-heuristic algorithm that simulates the growth of Tumbleweed. Compared with other advanced meta-heuristic algorithms, TA has strong optimization performance and competitiveness. However, TA is a continuous optimization algorithm, which is not suitable for binary problems. This paper presents a binary Tumbleweed optimization algorithm (BTA). The continuous search space is mapped into the discrete space by V-shaped and S-shaped transfer functions. In addition, this paper uses the CEC2013 benchmark function to evaluate the efficiency of BTA. To prove the effectiveness of the proposed algorithm, the BTA is compared with four advanced binary optimization algorithms. The experimental results reflect that BTA has preferable optimization capabilities on unimodal, multimodal, and composite functions. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Heuristic algorithms

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