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 potential of quantum-inspired optimization techniques, specifically leveraging concepts from quantum annealing, to accelerate the graph partitioning process. We formulate the graph partitioning problem as a Quadratic Unconstrained Binary Optimization (QUBO) problem, allowing us to simulate the behavior of a quantum annealer on classical hardware. The core of our approach involves mapping the graph's connectivity structure onto a QUBO, representing nodes as binary variables and edges as constraints. By exploiting the inherent parallelism of quantum annealing, we aim to find optimal or near-optimal partitions significantly faster than conventional methods. The results demonstrate the feasibility and potential advantages of this quantum-inspired strategy for graph partitioning, offering a promising direction for tackling computationally intensive graph problems.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
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 utilization and overall system performance. The proposed system constructs a "Resource Management Network" comprised of several agents, each dedicated to managing a specific resource type. These agents communicate and make decisions via defined protocols, enabling a highly adaptable and robust scheduling process. The system leverages the strengths of multi-agent collaboration to improve the flexibility and resilience of traditional centralized scheduling methods. This research addresses the limitations of static, pre-defined scheduling policies by introducing a dynamic, self-organizing system capable of responding to real-time conditions and evolving demands. The presented architecture provides a scalable solution for diverse distributed systems, including cloud computing, IoT networks, and edge computing environments. Further investigation into performance metrics and potential extensions is warranted. ---
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
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 resources are registered, shared, and traded through a blockchain-based platform. The core claim is that utilizing blockchain enhances the security and reliability of edge computing resource sharing. The proposed mechanism utilizes the distributed ledger technology inherent in blockchain to create a transparent and tamper-proof record of resource transactions. This ensures trust among participants and facilitates efficient resource allocation. The system's architecture incorporates key elements such as smart contracts for automated resource management and consensus mechanisms for validating transactions. This decentralized approach addresses the limitations of centralized models, offering a more robust and scalable solution for edge computing resource sharing. The research explores the potential of this system to improve resource utilization, reduce operational costs, and unlock new opportunities for edge computing applications. The efficacy of the system is examined through theoretical analysis and conceptual design, laying the groundwork for future implementation and experimentation.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
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 utilization and overall system performance. The proposed system constructs a "Resource Management Network" comprised of several agents, each dedicated to managing a specific resource type. These agents communicate and make decisions via defined protocols, enabling a highly adaptable and robust scheduling process. The system leverages the strengths of multi-agent collaboration to improve the flexibility and resilience of traditional centralized scheduling methods. This research addresses the limitations of static, pre-defined scheduling policies by introducing a dynamic, self-organizing system capable of responding to real-time conditions and evolving demands. The presented architecture provides a scalable solution for diverse distributed systems, including cloud computing, IoT networks, and edge computing environments. Further investigation into performance metrics and potential extensions is warranted. ---
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
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 them vulnerable to advancements in computing power, including quantum computers. Our approach aims to mitigate this vulnerability by employing a graph structure to represent potential communication partners and utilizing a probabilistic routing algorithm that mimics the superposition principle. This allows for the simultaneous evaluation of multiple routing paths, dramatically increasing the computational complexity for an attacker while simultaneously reducing latency. The core of the system is a dynamically constructed graph where nodes represent potential key exchange partners and edges represent communication channels with associated security scores calculated probabilistically. The algorithm then iteratively explores this graph, utilizing a 'superposition' effect to consider multiple paths concurrently. The path with the highest security score is selected, ensuring robust key exchange. This method offers a potentially more resilient key exchange solution against both classical and future quantum attacks. The performance of this approach is evaluated through theoretical analysis and the design of a simplified prototype, demonstrating its potential for enhanced security and efficiency.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
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 potential of quantum-inspired optimization techniques, specifically leveraging concepts from quantum annealing, to accelerate the graph partitioning process. We formulate the graph partitioning problem as a Quadratic Unconstrained Binary Optimization (QUBO) problem, allowing us to simulate the behavior of a quantum annealer on classical hardware. The core of our approach involves mapping the graph's connectivity structure onto a QUBO, representing nodes as binary variables and edges as constraints. By exploiting the inherent parallelism of quantum annealing, we aim to find optimal or near-optimal partitions significantly faster than conventional methods. The results demonstrate the feasibility and potential advantages of this quantum-inspired strategy for graph partitioning, offering a promising direction for tackling computationally intensive graph problems.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
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 utilizes knowledge graphs and logic programming to convert the internal representations of deep learning models into symbolic knowledge. The mechanism involves a process of mapping learned features from deep neural networks into a formal knowledge representation, followed by applying logical inference rules to derive new knowledge or explain existing predictions. The novelty lies in the deep fusion of these two approaches, offering a pathway to increased transparency and trustworthiness in AI systems. The proposed framework addresses the limitations of black-box deep learning models by providing a transparent, rule-based explanation of their decisions. This work contributes to the growing field of XAI by offering a concrete methodology for bridging the gap between the statistical power of deep learning and the interpretability of symbolic reasoning.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
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 utilizes knowledge graphs and logic programming to convert the internal representations of deep learning models into symbolic knowledge. The mechanism involves a process of mapping learned features from deep neural networks into a formal knowledge representation, followed by applying logical inference rules to derive new knowledge or explain existing predictions. The novelty lies in the deep fusion of these two approaches, offering a pathway to increased transparency and trustworthiness in AI systems. The proposed framework addresses the limitations of black-box deep learning models by providing a transparent, rule-based explanation of their decisions. This work contributes to the growing field of XAI by offering a concrete methodology for bridging the gap between the statistical power of deep learning and the interpretability of symbolic reasoning.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
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 interpretability, leading to uncertainties in control outcomes. This research addresses this limitation by integrating causality into XAI, aiming to enhance the accuracy and reliability of intervention strategies. The methodology focuses on understanding the 'what' and 'why' behind interventions through causal reasoning, offering a pathway to more trustworthy and explainable AI systems. The presented framework provides a robust mechanism for evaluating and refining intervention policies, ultimately contributing to a greater degree of transparency and control in AI-driven decision-making. This work emphasizes the importance of causal reasoning in building truly intelligent and accountable AI systems.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
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 the development of methods for achieving algorithmic transparency in deep neural networks by generating interpretable representations. We propose a framework that combines representation learning techniques with visualization tools to enable users to understand the internal workings of these networks. The core of our approach lies in designing architectures that explicitly encourage the learning of human-interpretable features, thereby facilitating trust and accountability in deep learning systems. This work directly addresses the need for explainable AI (XAI) and offers a pathway towards building more reliable and understandable deep neural network models. The primary goal is to provide a systematic approach to extracting meaningful insights from complex models, ultimately contributing to a more robust and trustworthy AI landscape.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
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 inference and decision-making processes. This fusion aims to create a system capable of generating more reliable and interpretable intelligent decisions. We introduce a framework where neural networks represent observational data and learn underlying causal structures, which are then formalized and manipulated using symbolic logic for reasoning. The resulting system offers a significant advancement over traditional approaches by addressing the complexities inherent in causal inference and ultimately enhancing the trustworthiness and explainability of intelligent decision-making systems. The paper details the architecture of the proposed system, the methods used for neural-symbolic integration, and outlines potential applications. ---
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
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 facilitate probabilistic inference and decision-making. This hybrid system aims to overcome limitations inherent in purely neural or purely symbolic approaches, leading to more robust and explainable reasoning capabilities. The paper details the architecture, the learning process, and the inference mechanism of this neuro-symbolic framework. We demonstrate the potential of this approach through a conceptual design, focusing on the integration of learned symbolic knowledge into a Bayesian network for improved reasoning performance. The key contribution lies in the systematic combination of neural representation learning with Bayesian network inference, providing a path toward more reliable and interpretable AI systems.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
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