A Minus Dominating (MD) Function of a graph G = (V, E) (vertical bar V vertical bar = n) is a function that assigns a value from {-1, 0, 1} to each node i. V such that the sum of the values of node i and all its neigh...
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ISBN:
(纸本)9783031744976;9783031744983
A Minus Dominating (MD) Function of a graph G = (V, E) (vertical bar V vertical bar = n) is a function that assigns a value from {-1, 0, 1} to each node i. V such that the sum of the values of node i and all its neighboring nodes is positive (i.e., equal to or greater than 1). An MD function is minimal if decreasing the value of any node by 1 causes a violation of the conditions of the MD function. As an extension of the MD function, we introduce the k-Minimal Minus Dominating (MMD) Function (k >= 0), which is a minimal MD function such that no other MD function can be obtained by increasing the values for some nodes by k in total and decreasing the values for some nodes by at least k + 1 in total. Note that any minimal MD function can be referred to as a 0-MMD function. In this paper, we propose a silent self-stabilizing algorithm to solve the 1-Minimal Minus Domination Problem on an arbitrary graph, using a composition technique that repeatedly applies several self-stabilizing algorithms in order, known as loop composition. It converges within O(n(Delta(2) + D)) rounds, where D is the diameter and Delta is the maximum degree of a graph, and each node requires O(Delta(4) log n) bits of memory.
Threshold automata are a computational model that has proven to be versatile in modeling threshold-based distributed algorithms and enabling their completely automatic parameterized verification. We present novel tech...
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ISBN:
(纸本)9783031711619;9783031711626
Threshold automata are a computational model that has proven to be versatile in modeling threshold-based distributed algorithms and enabling their completely automatic parameterized verification. We present novel techniques for the verification of threshold automata, based on well-structured transition systems, that allow us to extend the expressiveness of both the computational model and the specifications that can be verified. In particular, we extend the model to allow decrements and resets of shared variables, possibly on cycles, and the specifications to general coverability. While these extensions of the model in general lead to undecidability, our algorithms provide a semi-decision procedure. We demonstrate the benefit of our extensions by showing that we can model complex round-based algorithms such as the phase king consensus algorithm and the Red Belly Blockchain protocol (published in 2019), and verify them fully automatically for the first time.
Over the past three decades, wireless communication has seen a significant surge, with Vehicular networks emerging as a prominent area of interest for researchers. Managing mobility in these networks presents a consid...
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ISBN:
(纸本)9783031829307;9783031829314
Over the past three decades, wireless communication has seen a significant surge, with Vehicular networks emerging as a prominent area of interest for researchers. Managing mobility in these networks presents a considerable challenge. Vehicles moving between roadside units or base stations can experience disruptions in real-time services such as Voice over IP (VoIP), media streaming, safety-critical data transmission, and gaming. Due to the typically high speed of vehicles, these movements often result in handovers and packet loss. This research introduces a novel approach to mobility management and handover execution for mobile vehicles travelling on highways, differing from traditional methods. The proposed model predicts a vehicle's movement from one base station to the next using a probabilistic method by the correspondent node, which then initiates the binding refresh procedure accordingly. This approach reduces control traffic overhead and packet loss, leading to more efficient use of limited wireless resources. The performance of the proposed algorithm has been evaluated through simulation, demonstrating its effectiveness in reducing packet loss, handover delay, and control traffic overhead.
Water is a significant resource in day-to-day life, and it usually requires technological association for comprehensive management. Smart Water Grids (SWG) typically use cyberphysical systems (CPS) to monitor several ...
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Underwater wireless sensor networks can monitor ocean information, which provides a new approach to marine environmental monitoring, disaster warning and resource exploration. However, the development of underwater wi...
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After adopting 5G technology, businesses and academia have started working on sixth-generation wireless networking (6G) technologies. Mobile communications options are expected to expand in areas where previous genera...
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Data plane verification (DPV) can be critical in ensuring the network operates correctly. To be useful in practice, they need to be: (1) fast so as to prevent significant packet loss or security violations;(2) scalabl...
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Dance style recognition through video analysis during university training can significantly benefit both instructors and novice dancers. Employing video analysis in training offers substantial advantages, including th...
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Dance style recognition through video analysis during university training can significantly benefit both instructors and novice dancers. Employing video analysis in training offers substantial advantages, including the potential to train future dancers using innovative technologies. Over time, intricate dance gestures can be honed, reducing the burden on instructors who would, otherwise, need to provide repetitive demonstrations. Recognizing dancers' movements, evaluating and adjusting their gestures, and extracting cognitive functions for efficient evaluation and classification are pivotal aspects of our model. Deep learning currently stands as one of the most effective approaches for achieving these objectives, particularly with short video clips. However, limited research has focused on automated analysis of dance videos for training purposes and assisting instructors. In addition, assessing the quality and accuracy of performance video recordings presents a complex challenge, especially when judges cannot fully focus on the on-stage performance. This paper proposes an alternative to manual evaluation through a video-based approach for dance assessment. By utilizing short video clips, we conduct dance analysis employing techniques such as fine-grained dance style classification in video frames, convolutional neural networks (CNNs) with channel attention mechanisms (CAMs), and autoencoders (AEs). These methods enable accurate evaluation and data gathering, leading to precise conclusions. Furthermore, utilizing cloud space for real-time processing of video frames is essential for timely analysis of dance styles, enhancing the efficiency of information processing. Experimental results demonstrate the effectiveness of our evaluation method in terms of accuracy and F1-score calculation, with accuracy exceeding 97.24% and the F1-score reaching 97.30%. These findings corroborate the efficacy and precision of our approach in dance evaluation analysis.
Traditional decision support systems (DSS) show obvious limitations in dealing with increasingly complex and dynamic decision-making scenarios. By integrating graph neural networks (GNNs) and expert systems (ESs), thi...
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The proceedings contain 10 papers. The special focus in this conference is on Security and Privacy in Social networks and Big Data. The topics include: ChatGPT Through the Users’ Eyes: Sentiment Analysis of ...
ISBN:
(纸本)9789819637737
The proceedings contain 10 papers. The special focus in this conference is on Security and Privacy in Social networks and Big Data. The topics include: ChatGPT Through the Users’ Eyes: Sentiment Analysis of Privacy and Security Issues;Extended Abstract: Evaluating Cross-Chain Platforms for EV Charging Payment systems;Using M5Stack Core 2 ESP32 to Raise Children’s Awareness About Cyber Security;ELITE: Efficient and Secure Machine Learning for Intelligent Perception in Smart Road Infrastructure;gamma Sampling for Intrusion Detection with Imbalanced Data;smartAudit: Smart Contract Vulnerability Detection Using Transfer Learning;FLAIR: A Federated Learning Approach Against Inference Attacks and Risks;Secure Aggregation of Smartwatch Health Data with LDP.
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