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Showing 1 - 20 results of 116 for search '(( python code represents ) OR ( python work represents ))', query time: 0.44s Refine Results
  1. 1
    Multi-Version PYZ Builder Script: A Universal Python Module Creation Tool

    Multi-Version PYZ Builder Script: A Universal Python Module Creation Tool by Pavel Izosimov (20096259)

    Published 2024
    “…This tool represents a significant advancement in the realm of <a href="https://xn--mxac.net/secure-python-code-manager.html" target="_blank"><b>secure code sharing</b></a>, providing a robust solution for modern Python programming challenges.…”
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  2. 2
    How RAG work.

    How RAG work. by Jin Lu (428513)

    Published 2025
    “…Additionally, the integration of an LLM Code Interpreter enables the system to perform multi-step logical reasoning and execute Python code for precise calculations, significantly improving its ability to solve mathematical problems and handle complex queries. …”
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  3. 3
    System Hardware ID Generator Script: A Cross-Platform Hardware Identification Tool

    System Hardware ID Generator Script: A Cross-Platform Hardware Identification Tool by Pavel Izosimov (20096259)

    Published 2024
    “…This tool provides <a href="https://xn--mxac.net/local-python-code-protector.html" target="_blank">code obfuscation in Python</a> and <a href="https://xn--mxac.net/secure-python-code-manager.html" target="_blank">Python code encryption</a>, enabling developers to <a href="https://xn--mxac.net/local-python-code-protector.html" target="_blank">protect Python code</a> effectively.…”
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  4. 4
    Illustration of the text transformation process before and after synonym replacement: The process begins with a sample initial text segment (Sample text before synonym replacement’), followed by a word frequency count (‘Counter’).

    Illustration of the text transformation process before and after synonym replacement: The process begins with a sample initial text segment (Sample text before synonym replacement’... by Enock Niyonkuru (21246879)

    Published 2025
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  5. 5
    Schematic of the approach: This schematic illustrates the entire workflow of the project.

    Schematic of the approach: This schematic illustrates the entire workflow of the project. by Enock Niyonkuru (21246879)

    Published 2025
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  6. 6
    Text Transformation Pipeline: An example of the multi-stage text transformation pipeline applied to a sample abstract (PMID: 30609739).

    Text Transformation Pipeline: An example of the multi-stage text transformation pipeline applied to a sample abstract (PMID: 30609739). by Enock Niyonkuru (21246879)

    Published 2025
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  7. 7
    Comparative analysis of WordNet replacement impact on data distribution.

    Comparative analysis of WordNet replacement impact on data distribution. by Enock Niyonkuru (21246879)

    Published 2025
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  8. 8
    Comparison of window size for embedding with Wordnet synonym replacement (WN) and without (PM). While Table 1 compared the effects of different values of using a window size of 10, this Table shows the results for three different window sizes at a =. Abbreviations are the same as for Table 1.

    Comparison of window size for embedding with Wordnet synonym replacement (WN) and without (PM). While Table 1 compared the effects of different values of using a window size of 10,... by Enock Niyonkuru (21246879)

    Published 2025
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  9. 9
    Non-biomedical word replacement algorithm: This algorithm outlines the process for replacing non-biomedical words in a corpus using WordNet.

    Non-biomedical word replacement algorithm: This algorithm outlines the process for replacing non-biomedical words in a corpus using WordNet. by Enock Niyonkuru (21246879)

    Published 2025
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  10. 10
    Comparison of mean interconcept distance for embedding with WordNet synonym replacement (WN) and without (PM). The initial number of unique concepts in the total corpus was 3,018,918. The Table summarizes results for different thresholds () and categories of concept/gene sets (M,B,K,G,P). Columns: : Replacement threshold; replaced: Unique Replaced Concepts; Category: M = MeSH, B = Biocarta, K = KEGG, G = GP(bp), P = PID; # sets: Number of concept/gene sets in the categories; #Concepts: number of concept vectors in the category; <i>WN better</i><i>: The count and percentage of concept/gene sets for which the mean interconcept distance was smaller for WN than for PM. “Winners” are shown in bold.;</i> <i>PM better</i><i>: Analogous to “WN better” but for PM.</i>

    Comparison of mean interconcept distance for embedding with WordNet synonym replacement (WN) and without (PM). The initial number of unique concepts in the total corpus was 3,018,9... by Enock Niyonkuru (21246879)

    Published 2025
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  11. 11
    Comparative analysis of WN and PM methodologies: Figure (a) displays the bar chart comparing WN and PM across five distinct concept sets (Methods), highlighting the number of concept sets where the cluster mean distance is significantly lower, indicative of superior embeddings.

    Comparative analysis of WN and PM methodologies: Figure (a) displays the bar chart comparing WN and PM across five distinct concept sets (Methods), highlighting the number of conce... by Enock Niyonkuru (21246879)

    Published 2025
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  12. 12
    Code interpreter with LLM.

    Code interpreter with LLM. by Jin Lu (428513)

    Published 2025
    “…Additionally, the integration of an LLM Code Interpreter enables the system to perform multi-step logical reasoning and execute Python code for precise calculations, significantly improving its ability to solve mathematical problems and handle complex queries. …”
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  13. 13
    Datasets To EVAL.

    Datasets To EVAL. by Jin Lu (428513)

    Published 2025
    “…Additionally, the integration of an LLM Code Interpreter enables the system to perform multi-step logical reasoning and execute Python code for precise calculations, significantly improving its ability to solve mathematical problems and handle complex queries. …”
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  14. 14
    Statistical significance test results.

    Statistical significance test results. by Jin Lu (428513)

    Published 2025
    “…Additionally, the integration of an LLM Code Interpreter enables the system to perform multi-step logical reasoning and execute Python code for precise calculations, significantly improving its ability to solve mathematical problems and handle complex queries. …”
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  15. 15
    OpenBookQA experimental results.

    OpenBookQA experimental results. by Jin Lu (428513)

    Published 2025
    “…Additionally, the integration of an LLM Code Interpreter enables the system to perform multi-step logical reasoning and execute Python code for precise calculations, significantly improving its ability to solve mathematical problems and handle complex queries. …”
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  16. 16
    AI2_ARC experimental results.

    AI2_ARC experimental results. by Jin Lu (428513)

    Published 2025
    “…Additionally, the integration of an LLM Code Interpreter enables the system to perform multi-step logical reasoning and execute Python code for precise calculations, significantly improving its ability to solve mathematical problems and handle complex queries. …”
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  17. 17
    TQA experimental results.

    TQA experimental results. by Jin Lu (428513)

    Published 2025
    “…Additionally, the integration of an LLM Code Interpreter enables the system to perform multi-step logical reasoning and execute Python code for precise calculations, significantly improving its ability to solve mathematical problems and handle complex queries. …”
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  18. 18
    E-EVAL experimental results.

    E-EVAL experimental results. by Jin Lu (428513)

    Published 2025
    “…Additionally, the integration of an LLM Code Interpreter enables the system to perform multi-step logical reasoning and execute Python code for precise calculations, significantly improving its ability to solve mathematical problems and handle complex queries. …”
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  19. 19
    TQA Accuracy Comparison Chart on different LLM.

    TQA Accuracy Comparison Chart on different LLM. by Jin Lu (428513)

    Published 2025
    “…Additionally, the integration of an LLM Code Interpreter enables the system to perform multi-step logical reasoning and execute Python code for precise calculations, significantly improving its ability to solve mathematical problems and handle complex queries. …”
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  20. 20
    ScienceQA experimental results.

    ScienceQA experimental results. by Jin Lu (428513)

    Published 2025
    “…Additionally, the integration of an LLM Code Interpreter enables the system to perform multi-step logical reasoning and execute Python code for precise calculations, significantly improving its ability to solve mathematical problems and handle complex queries. …”
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