The network link-layer topology, also known as the physical topology, describes the networking hardware devices, their corresponding placement, and the interconnection between them. Discovering and maintaining updated...
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This paper investigates the physical-layer security (PLS) for an overlay cognitive radio network (CRN). Due to the unreliability of direct communication between the primary users (PUs), secondary users (SUs) are enlis...
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This paper explores the application of a federated learning-based multi-agent reinforcement learning (MARL) strategy to enhance physical-layer security (PLS) in a multi-cellular network within the context of beyond 5G...
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ISBN:
(数字)9781728190549
ISBN:
(纸本)9781728190556
This paper explores the application of a federated learning-based multi-agent reinforcement learning (MARL) strategy to enhance physical-layer security (PLS) in a multi-cellular network within the context of beyond 5G networks. At each cell, a base station (BS) operates as a deep reinforcement learning (DRL) agent that interacts with the surrounding environment to maximize the secrecy rate of legitimate users in the presence of an eavesdropper. This eavesdropper attempts to intercept the confidential information shared between the BS and its authorized users. The DRL agents are deemed to be federated since they only share their network parameters with a central server and not the private data of their legitimate users. Two DRL approaches, deep Q-network (DQN) and Reinforce deep policy gradient (RDPG), are explored and compared. The results demonstrate that RDPG converges more rapidly than DQN. In addition, we demonstrate that the proposed method outperforms the distributed DRL approach. Furthermore, the outcomes illustrate the trade-off between security and complexity.
Tracking the evolution of smart contracts is challenging due to their immutable nature and complex upgrade mechanisms. We introduce EvoChain, a comprehensive framework and dataset designed to track and visualize smart...
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ISBN:
(数字)9798331501839
ISBN:
(纸本)9798331501846
Tracking the evolution of smart contracts is challenging due to their immutable nature and complex upgrade mechanisms. We introduce EvoChain, a comprehensive framework and dataset designed to track and visualize smart contract evolution. Building upon data from our previous empirical study, EvoChain models contract relationships using a Neo4j graph database and provides an interactive web interface for exploration. The framework consists of a data layer, an API layer, and a user interface layer. EvoChain allows stakeholders to analyze contract histories, upgrade paths, and associated vulnerabilities by leveraging these components. Our dataset encompasses approximately 1.3 million upgradeable proxies and nearly 15,000 historical versions, enhancing transparency and trust in blockchain ecosystems by providing an accessible platform for understanding smart contract evolution.
Tracking the evolution of smart contracts is challenging due to their immutable nature and complex upgrade mechanisms. We introduce EvoChain, a comprehensive framework and dataset designed to track and visualize smart...
AI-powered code generation models have been developing rapidly, allowing developers to expedite code generation and thus improve their productivity. These models are trained on large corpora of code (primarily sourced...
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The P4 language has proven to be a powerful tool for programming packet processing, but its original design did not intend to handle stateful processing effectively. This shortcoming stems from the fact that the netwo...
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ISBN:
(数字)9798350369588
ISBN:
(纸本)9798350369595
The P4 language has proven to be a powerful tool for programming packet processing, but its original design did not intend to handle stateful processing effectively. This shortcoming stems from the fact that the network switches for which the language was designed have restricted memory capacities, which makes it challenging to manage complex stateful objects. As a result, P4’s syntax was not optimized for handling such objects. With contemporary networks increasingly relying on stateful processing and abstractions like Extended Finite State Machines (EFSMs), we propose extending P4’s syntax through an EFSM construct. This work aims to grant developers the ability to create streamlined and productive P4 programs that can effortlessly deal with stateful objects. This improvement holds great promise for expanding P4’s functionality and refining it to support stateful processing.
In the domain of formal verification, translating natural language (NL) requirements into Computation Tree Logic (CTL) specifications presents a notable challenge due to the disparity between human-readable documents ...
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ISBN:
(数字)9798331519537
ISBN:
(纸本)9798331519544
In the domain of formal verification, translating natural language (NL) requirements into Computation Tree Logic (CTL) specifications presents a notable challenge due to the disparity between human-readable documents and formal specifications. This paper introduces a novel approach that leverages Large Language Models (LLMs) to automate this translation process, thereby enhancing the accuracy and efficiency of formal verification practices. We fine-tune three state-of-the-art LLMs—LLAMA3, Mistral, and Qwen2—with a particular focus on optimizing the Mistral model due to its superior performance. Our methodology is supported by the Natural2CTL dataset, consisting of 2,095 NL requirements and their corresponding CTL specifications. We employ evaluation metrics such as validation loss, accuracy, semantic similarity, and Structural Operator Jaccard Similarity (SOJS) for a comprehensive assessment of model performance. Additionally, a comparative analysis with human translators, trained in CTL logic, underscores the LLMs’ potential to match or even surpass human accuracy in translating NL requirements into formal specifications. Our findings reveal that the fine-tuned Mistral model significantly outperforms the other LLMs and human participants, demonstrating superior accuracy in generating CTL specifications. This study advances the field of formal verification by proposing a scalable solution to the NL-to-CTL translation challenge, setting a new benchmark for the integration of AI tools in complex specification tasks.
作者：
Gosselin, FrancisZouaq, AmalLAMA-WeST Lab.
Departement of Computer Engineering and Software Engineering Polytechnique Montreal 2500 Chem. de Polytechnique MontréalQCH3T 1J4 Canada
This paper presents the results of SORBETMatcher in the OAEI 2023 competition. SORBETMatcher is a schema matching system for both equivalence matching and subsumption matching. SORBETMatcher is largely based on SORBET...
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In industrial environments, predictinghuman actions is essential for ensuring safe and effective collaboration between humans and robots. This paper introduces a perception framework that enables mobile robots to unde...
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