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Security Challenges and Solutions in Smart Cities 2nd

Security Challenges and Solutions in Smart Cities

引用

2nd International Conference on Advances in Computing Research, ACR 2024

作者： Alzyoud, Faisal Al-Falah, Ruba Tarawneh, Monther Tarawneh, Omar Computer Science Department Isra University Amman Jordan Ministry of Local Administration Amman Jordan Software Engineering Department Amman Arab University Amman Jordan

ISBN: (纸本)9783031569494

The increase of smart devices and vast amount of telecommunication growth raise the tendency to convert convolution cities in to emerging smart cities. This paper synthesizes key findings and insights from a comprehensive presentation, addressing critical aspects of smart city security. It underscores the pivotal role of robust security strategies in the development and sustainability of smart urban environments. This paper, encompass three main areas: the integration of Artificial Intelligence (AI) and machine learning for real-time threat detection, the imperative of adopting quantum-safe cryptography, and the evolving nature of security threats in smart cities. These trends and challenges necessitate proactive measures, reinforcing the need for security to be a fundamental component of smart city blueprints. In conclusion, this paper advocates for collaborative efforts and ongoing research in smart city security. It calls upon stakeholders from diverse sectors to join forces, share knowledge, and develop innovative solutions. By doing so, we can navigate the complex security landscape of smart cities and shape a future where technology and security coexist harmoniously, ensuring the well-being and prosperity of urban communities. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Smart city

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A Secured and Optimized Clustering Approch in Vehicular Ad-hoc Network 4

A Secured and Optimized Clustering Approch in Vehicular Ad-h...

引用

4th IEEE International Conference on Electrical, computer, and Energy Technologies, ICECET 2024

作者： Khan, Imran Khan, Fazlullah Ali, Asad Alshawi, Bandar Alturki, Ryan Wedyan, Mohammad Abdul Wali Khan University Computer Science Department KPK Mardan Pakistan School of Computer Science University of Nottingham Ningbo China Faculty of Science and Engineering Zhejiang315104 China University of Engineering and Technology Department of Computer Science KPK Peshawar Pakistan College of Computing Umm Al-Qura University Department of Computer and Network Engineering Makkah Saudi Arabia College of Computing Umm Al-Qura University Department of Software Engineering Makkah Saudi Arabia Faculty of Information Technology and Computer Sciences Department of Computer Sciences Irbid21163 Jordan

ISBN: (纸本)9798350395914

Vehicular ad hoc networks (VANETs) take vital role in intelligent transportation systems, but they face challenges due to dynamic network topology, impacting communication efficiency, especially with increasing active nodes. Security and privacy concerns are paramount, necessitating authentication to ensure only legitimate vehicles access the network. Secure vehicular clustering offers a solution, yet the number of clusters affects network dependability and efficiency. To tackle this, we present a secure clustering mechanism using Bat optimization algorithm, mimicking echolocation behavior to optimize clusters. We introduce an authentication technique for cluster heads to admit approved members, enhancing vehicular authentication. Simulations using MATLAB evaluate our approach, showing improved performance in end-to-end delay, overhead, and cluster head count compared to similar methods. © 2024 IEEE.

关键词： Vehicular ad hoc networks

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SConE: Simplified Cone Embeddings with Symbolic Operators for Complex Logical Queries 61

SConE: Simplified Cone Embeddings with Symbolic Operators fo...

引用

Findings of the Association for Computational Linguistics, ACL 2023

作者： Nguyen, Chau Duc Minh French, Tim Liu, Wei Stewart, Michael ARC Centre for Transforming Maintenance through Data Science Department of Computer Science and Software Engineering School of Physics Mathematics and Computing The University of Western Australia

ISBN: (纸本)9781959429623

Geometric representation of query embeddings (using points, particles, rectangles and cones) can effectively achieve the task of answering complex logical queries expressed in first-order logic (FOL) form over knowledge graphs, allowing intuitive encodings. However, current geometric-based methods depend on the neural approach to model FOL operators (conjunction, disjunction and negation), which are not easily explainable with considerable computation cost. We overcome this challenge by introducing a symbolic modeling approach for the FOL operators, emphasizing the direct calculation of the intersection between geometric shapes, particularly sector-cones in the embedding space, to model the conjunction operator. This approach reduces the computation cost as a non-neural approach is involved in the core logic operators. Moreover, we propose to accelerate the learning in the relation projection operator using the neural approach to emphasize the essential role of this operator in all query structures. Although empirical evidence for explainability is challenging, our approach demonstrates a significant improvement in answering complex logical queries (both non-negative and negative FOL forms) over previous geometric-based models. © 2023 Association for Computational Linguistics.

关键词： computer circuits

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Face Anti-spoofing based on Convolutional Neural Networks 3

Face Anti-spoofing based on Convolutional Neural Networks

引用

3rd International Conference on Intelligent Data science Technologies and Applications, IDSTA 2022

作者： Maphisa, Siyamdumisa Coulter, Duncan University of Johannesburg Academy of Computer Science and Software Engineering Johannesburg South Africa

ISBN: (数字)9781665499606

ISBN: (纸本)9781665499606

Biometrics technologies have gained increasing attention across different sectors in the past decade. Face recognition has proven to be one of these successful biometric technologies. For example, law enforcement uses face recognition for faster investigations, banks for identity confirmation, and different organisations for access control. However, face recognition has shortcomings regardless of its high successes, just like any biometrics technology. Face recognition technology is still susceptible to face spoofing attacks despite great efforts made by different researchers to combat such attacks. The study proposes an anti-spoofing model based on deep learning methods. Three different pipelines are implemented based on convolutional neural network (CNN) architecture. A hyper tuned baseline CNN, a convolutional neural network based on AlexNet architecture, and a neural network based on VGG16 architecture. The study benchmarked pipelines using the available face anti-spoofing detection datasets - the NUAA and CelebA datasets. The study measures these performance metrics for all the pipelines: accuracy, precision, recall, F1 score, AUC, and Roc curve. All three pipelines provided good results when tested against the selected datasets. © 2022 IEEE.

关键词： Face recognition

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Dynamic View Synthesis of Thin Structures with Short-term Movements from Monocular Videos Using Neural Radiance Fields 25

Dynamic View Synthesis of Thin Structures with Short-term Mo...

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25th International Conference on Digital Image Computing: Techniques and Applications, DICTA 2024

作者： Rajapaksha, Uchitha Laga, Hamid Diepeveen, Dean Bennamoun, Mohammed Sohel, Ferdous School of Information Technology Murdoch University MurdochWA Australia The University of Western Australia Dept. of Computer Science and Software Engineering CrawleyWA Australia

ISBN: (纸本)9798350379037

Learning to generate motions of thin structures such as plant leaves in dynamic view synthesis is challenging. This is because thin structures usually undergo small but fast, non-rigid motions as they interact with air and wind. When given a set of RGB images or videos of a scene with moving thin structures as input, existing methods that map the scene to its corresponding canonical space for rendering novel views fail as the object movements are too subtle compared to the background. Disentangling the objects with thin parts from the background scene is also challenging when the parts show fast and rapid motions. To address these issues, we propose a Neural Radiance Field (NeRF)-based framework that accurately reconstructs thin structures such as leaves and captures their subtle, fast motions. The framework learns the geometry of a scene by mapping the dynamic images to a canonical scene in which the scene remains static. We propose a ray masking network to further decompose the canonical scene into foreground and background, thus enabling the network to focus more on foreground movements. We conducted experiments using a dataset containing thin structures such as leaves and petals, which include image sequences collected by us and one public image sequence. Experiments show superior results compared to existing methods. Video outputs are available at https://***/. © 2024 IEEE.

关键词： Rendering (computer graphics)

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Semantic Overlap Summarization among Multiple Alternative Narratives: an Exploratory Study 29

Semantic Overlap Summarization among Multiple Alternative Na...

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29th International Conference on Computational Linguistics, COLING 2022

作者： Bansal, Naman Akter, Mousumi Karmaker, Shubhra Kanti Lab Department of Computer Science and Software Engineering College of Engineering Auburn University United States

In this paper, we introduce an important yet relatively unexplored NLP task called Semantic Overlap Summarization (SOS), which entails generating a single summary from multiple alternative narratives which can convey the common information provided by those narratives. As no benchmark dataset is readily available for this task, we created one by collecting 2, 925 alternative narrative pairs from the web and then, went through the tedious process of manually creating 411 different reference summaries by engaging human annotators. As a way to evaluate this novel task, we first conducted a systematic study by borrowing the popular ROUGE metric from text-summarization literature and discovered that ROUGE is not suitable for our task. Subsequently, we conducted further human annotations to create 200 document-level and 1, 518 sentence-level ground-truth overlap labels. Our experiments show that the sentence-wise annotation technique with three overlap labels, i.e., {Absent (A), Partially-Present (PP), and Present (P)}, yields a higher correlation with human judgment and higher inter-rater agreement compared to the ROUGE metric. © 2022 Proceedings - International Conference on Computational Linguistics, COLING. All rights reserved.

关键词： Semantics

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Deep Structured Clustering of Short Text 9th

Deep Structured Clustering of Short Text

引用

9th CCF Conference on Big Data, BigData 2021

作者： Wu, Junxian Chen, Xiaojun Cai, Shaotian Li, Yongqi Wu, Huzi College of Computer Science and Software Engineering Shenzhen University Shenzhen518000 China

ISBN: (纸本)9789811697081

Short text clustering is beneficial in many applications such as articles recommendations, user clustering and event exploration. Recent works of short text clustering boost the clustering results by improving the representation of short text with deep neural networks, such as CNN and autoencoder. However, existing short text deep clustering methods ignore the structure information of short texts. In this paper, we present a GCN-based clustering method for short text clustering, named as Deep Structured Clustering (DSC) method, to explore the relationships among short texts for representation learning. We first construct a k -nn graph to capture the relationships among the short texts, and then jointly learn the short text representations and perform clustering with a dual self-supervised learning module. The experimental results demonstrate the superiority of our proposed method, and the ablation experimental results verify the effectiveness of the modules in our proposed method. © 2022, Springer Nature Singapore Pte Ltd.

关键词： Cluster analysis

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Accurate 3D Grapevine Structure Extraction from High-Resolution Point Clouds

Accurate 3D Grapevine Structure Extraction from High-Resolut...

引用

International Conference on Image and Vision Computing New Zealand, IVCNZ

作者： Harry Dobbs Casey Peat Oliver Batchelor James Atlas Richard Green Department of Computer Science and Software Engineering University of Canterbury

ISBN: (数字)9798331518776

ISBN: (纸本)9798331518783

Accurate 3D modelling of grapevines is crucial for precision viticulture, particularly for informed pruning decisions and automated management techniques. However, the intricate structure of grapevines poses significant challenges for traditional skeletonization algorithms. This paper presents an adaptation of the Smart-Tree algorithm for 3D grapevine modelling, addressing the unique characteristics of grapevine structures. We introduce a graph-based method for disambiguating skeletonization. Our method delineates individual cane skeletons, which are crucial for precise analysis and management. We validate our approach using annotated real-world grapevine point clouds, demonstrating an improvement of 15.8% in the F1 score compared to the original Smart-Tree algorithm. This research contributes to advancing 3D grapevine modelling techniques, potentially enhancing both the sustainability and profitability of grape production through more precise and automated viticulture practices.

关键词： Point cloud compression Adaptation models Solid modeling Three-dimensional displays Accuracy Profitability Pipelines Skeleton Yield estimation Sustainable development

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EPipe: Pipeline Inference Framework with High-quality Offline Parallelism Planning for Heterogeneous Edge Devices 24

EPipe: Pipeline Inference Framework with High-quality Offlin...

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43rd International Conference on computer-Aided Design, ICCAD 2024

作者： Xiong, Yi Liu, Weihong Zhang, Rui Zu, Yulong Zhu, Zongwei Zhou, Xuehai School of Computer Science and Technology University of Science and Technology of China China Suzhou Institute for Advanced Research University of Science and Technology of China China School of Software Engineering University of Science and Technology of China China

ISBN: (纸本)9798400710773

Pipeline parallelism is essential for edge computing as it effectively consolidates the limited resources of edge devices, enabling the deployment of large Deep Neural Network (DNN) models and accelerating inference processes without compromising the performance of models. Accurate computation and communication latency estimation on heterogeneous edge devices is essential for searching for a superior parallelism plan. However, existing heterogeneous pipeline inference approaches either incur substantial resource wastage during online parallelism planning, as they utilize profiling strategies that occupy physical devices;or rely on cost models with inadequate representational capabilities, leading to inaccurate predictions, thereby harming the result of pipeline planning. This paper proposes EPipe, a novel pipeline inference framework that supports high-quality offline planning in heterogeneous edge environments. EPipe integrates two core components: the Task-Device Co-analyzer (TDC) and the Multi-pipeline Parallelism Planner (MPP). TDC utilizes an undirected connected graph to depict the compatibility of DNNs across device groups and precisely estimates inference and communication latencies through fine-grained modeling. Based on TDC, MPP utilizes a dynamic programming-based genetic algorithm to explore multi-pipeline solutions, extending beyond traditional single-pipeline methods. A comprehensive experimental evaluation on an edge testbed confirms the effectiveness of EPipe, demonstrating significant speedups in inference tasks for both task streams and single tasks. © 2024 Copyright is held by the owner/author(s).

关键词： Edge detection

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ASIMSA: Advanced Semantic Information Guided Multi-Scale Alignment Framework for Medical Vision-Language Pretraining 9

ASIMSA: Advanced Semantic Information Guided Multi-Scale Ali...

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9th IEEE International Conference on Computational Intelligence and Applications, ICCIA 2024

作者： Xiao, Shuai Zhang, Yangyang Jiang, Liming Wang, Zhengxia School of Computer Science and Technology Hainan University China School of Information and Communication Engineering Hainan University China Software Co. Ltd. China

ISBN: (纸本)9798350352214

Medical Visual Language Pretraining (MVLP) utilizes textual reports for weak supervision to improve the learning of medical visual representations, showing promise in various medical image analysis tasks. While previous studies have focused on image and report correspondences within the same instances, they have overlooked high-level semantic associations, such as disease-level semantic correspondences, across different instances. To address this issue, we propose an Advanced Semantic Information Guided Multi-Scale Alignment Framework (ASIMSA) with two main modules: 1) Image-Report Alignment (IRA) module for learning shared representations between image and report of the same instance, and 2) Disease-Entity Alignment (DEA) module for learning disease entity correspondences across different instances. Extensive experiments on zero-shot classification, fine-tune classification, and semantic segmentation tasks validate the effectiveness of our proposed method, demonstrating its stable and superior performance. © 2024 IEEE.

关键词： Semantic Segmentation

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