This paper explores a novel approach to program self-optimization, termed "Entropy-Driven Program Self-Optimization (EDPSO)." The core concept revolves around a program's ability to monitor and adapt its own execution based on the inherent information entropy within its processes. The system employs an "entropy-aware" module to continuously measure the entropy during program execution. This entropy value then acts as a feedback signal, guiding the adjustment of the program's code structure. Genetic algorithms and/or reinforcement learning are utilized to optimize the code, driven by the dynamic entropy feedback. The key innovation lies in the program's intrinsic understanding of its own performance, shifting away from solely relying on external metrics. The proposed EDPSO framework presents a potentially powerful methodology for enhancing program efficiency and adaptability, particularly in complex and evolving environments. This paper details the architecture, the entropy measurement methodology, and the optimization algorithms utilized within EDPSO, outlining a pathway towards truly self-optimizing software.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
This paper introduces Dynamic Topological Dependency Learning (TDTL), a novel approach to knowledge representation and reasoning that leverages reinforcement learning to dynamically construct and adapt an internal knowledge graph based on observed data and inference results. Unlike traditional methods that rely on pre-defined topologies or manual annotation, TDTL autonomously learns data dependencies through a 'topological optimization' algorithm. The system begins with a simple, undirected graph and iteratively modifies it based on prediction errors, guided by a reward signal. A 'topological regularization' mechanism is incorporated to prevent over-complexity and maintain graph connectivity. The core claim is that a system can automatically build and adjust its internal knowledge representation's topology to reflect dynamic data dependencies without pre-defined topologies or manual labels. This represents a significant departure from existing knowledge graph construction and reasoning techniques. ---
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
Automated theorem proving (ATP) aims to develop systems capable of mechanically proving mathematical theorems. Despite significant advancements, ATP systems often struggle with complex reasoning tasks, largely due to the inherent difficulty in representing and executing logical deduction rules. This work proposes a novel approach to ATP that integrates neural networks to provide guidance during the proof process. The core idea is to train a neural network to suggest promising proof steps and identify relevant theorems, essentially acting as an "intelligent assistant" for the ATP system. This guidance mechanism is expected to improve the efficiency and effectiveness of ATP, particularly in tackling challenging mathematical problems. The presented framework utilizes a reinforcement learning approach, where the neural network learns to optimize the proof strategy based on the current state of the proof and the available theorems. The system is evaluated conceptually, outlining the architecture and training process, and highlighting potential improvements. Further research will focus on developing and refining the network architecture, exploring different training strategies, and integrating the guidance mechanism with existing ATP systems.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
This paper presents a novel dynamic probability field model designed to simulate complex system behavior and predict future states by dynamically adjusting probability field parameters based on real-time data and environmental changes. Leveraging the integration of machine learning and reinforcement learning, this model offers a significant advancement over traditional probabilistic models, enabling automated parameter tuning and real-time system modeling. The core mechanism focuses on continuously adapting probability field configurations, leading to enhanced prediction accuracy and efficiency. This work addresses a critical need for systems capable of robust and adaptable behavior in dynamic environments.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
This paper introduces the Dynamic Neural Network Topology (DNTN), a novel neural network architecture designed to overcome the limitations of static, connection-based networks. The core claim of this work is that by dynamically adjusting the physical connection strengths and topology of neurons in real-time, adaptive learning and memory capabilities can be achieved, surpassing the constraints of traditional neural networks. The proposed DNTN utilizes a Microelectromechanical Systems (MEMS) array as a neuron substrate, with each MEMS structure representing a neuron. A reinforcement learning controller dynamically adjusts connection parameters based on task objectives and environmental feedback, optimizing network structure and function. Furthermore, a metabolic module mimics biological neuron energy consumption, preventing excessive connections and network degradation. The DNTN represents a significant advancement in neural network design, offering enhanced learning efficiency, flexibility, and a closer simulation of biological neural systems. Key characteristics include dynamic topology reconfiguration, real-time adaptation, and a biologically inspired metabolic control mechanism.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
Automated theorem proving (ATP) aims to develop systems capable of mechanically proving mathematical theorems. Despite significant advancements, ATP systems often struggle with complex reasoning tasks, largely due to the inherent difficulty in representing and executing logical deduction rules. This work proposes a novel approach to ATP that integrates neural networks to provide guidance during the proof process. The core idea is to train a neural network to suggest promising proof steps and identify relevant theorems, essentially acting as an "intelligent assistant" for the ATP system. This guidance mechanism is expected to improve the efficiency and effectiveness of ATP, particularly in tackling challenging mathematical problems. The presented framework utilizes a reinforcement learning approach, where the neural network learns to optimize the proof strategy based on the current state of the proof and the available theorems. The system is evaluated conceptually, outlining the architecture and training process, and highlighting potential improvements. Further research will focus on developing and refining the network architecture, exploring different training strategies, and integrating the guidance mechanism with existing ATP systems.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
This paper presents a novel approach to software security protocol design leveraging formal verification techniques. Traditional protocol design relies heavily on manual analysis and testing, which are often insufficient to guarantee complete security. This work introduces a methodology that transforms security protocols into formal models, allowing for rigorous verification using formal verification tools. The core claim is that by employing formal verification, we can achieve a significantly higher level of confidence in the security of a protocol compared to conventional methods. The proposed mechanism involves the translation of protocol specifications into mathematical models, followed by automated verification to identify potential vulnerabilities and ensure adherence to security properties. This approach promises to improve the robustness and reliability of security protocols, particularly in critical systems where security breaches can have severe consequences. The research contributes to a paradigm shift in protocol design, moving from reactive testing to proactive verification.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
