153 papers indexed here
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Graph partitioning is a fundamental problem in various fields, including computer graphics, high-performance computing, and network design. Traditional graph partitioning algorithms often suffer from high computational complexity, particularly when dealing with large and complex graphs. This research investigates the p...
This paper presents a novel approach to distributed resource scheduling utilizing a multi-agent collaborative framework. The core claim is that through the orchestrated interaction of multiple intelligent agents, dynamic and optimized scheduling of distributed resources can be achieved, ultimately enhancing resource ut...
This paper proposes a novel approach to edge computing resource sharing leveraging blockchain technology. Traditional edge computing platforms often suffer from centralized control, leading to vulnerabilities and a suboptimal utilization of resources. This research introduces a decentralized system where edge computing...
This paper presents a novel approach to distributed resource scheduling utilizing a multi-agent collaborative framework. The core claim is that through the orchestrated interaction of multiple intelligent agents, dynamic and optimized scheduling of distributed resources can be achieved, ultimately enhancing resource ut...
This paper proposes a novel cryptographic key exchange protocol leveraging concepts inspired by quantum mechanics, specifically superposition and probabilistic exploration, through a graph-based routing mechanism. Traditional key exchange protocols often rely on computationally intensive mathematical problems, making t...
Graph partitioning is a fundamental problem in various fields, including computer graphics, high-performance computing, and network design. Traditional graph partitioning algorithms often suffer from high computational complexity, particularly when dealing with large and complex graphs. This research investigates the p...
This paper proposes a framework for achieving explainable artificial intelligence (XAI) by integrating deep learning with symbolic reasoning. The core claim is to translate the predictions of deep learning models into interpretable symbolic rules and then leverage these rules for symbolic reasoning. This framework util...
This paper proposes a framework for achieving explainable artificial intelligence (XAI) by integrating deep learning with symbolic reasoning. The core claim is to translate the predictions of deep learning models into interpretable symbolic rules and then leverage these rules for symbolic reasoning. This framework util...
This paper proposes a novel approach to explainable artificial intelligence (XAI) by leveraging causal intervention methods. The core idea is to model systems using causal graphs and then employ techniques like do-calculus for precise intervention and control. Traditional intervention methods often lack interpretabilit...
Deep neural networks have achieved remarkable success in various domains, including image recognition, natural language processing, and game playing. However, their inherent complexity often leads to a "black box" problem, where the reasoning behind their predictions remains opaque to humans. This research investigates...
This paper presents a novel approach to causal reasoning and decision-making by integrating the strengths of neural and symbolic artificial intelligence. The core idea is to leverage the pattern recognition capabilities of neural networks to learn causal relationships while utilizing symbolic reasoning for logical infe...
This paper proposes a novel approach to reasoning in complex domains by integrating the strengths of neural networks and Bayesian networks. The core idea is to leverage neural networks for feature extraction and symbolic representation learning, subsequently feeding these representations into a Bayesian network to faci...
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