Reversible data hiding in images has become one of the research hotspots in the field of information hiding. JPEG images are widely used on social networks, and the research on reversible information hiding technology...
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In order to improve the performance of Web service recommendation in small world heterogeneous network, this paper proposes a method based on reputation evaluation. The method defines the calculation method of reputat...
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Smart contracts are integral to blockchain's growth, but their vulnerabilities pose a significant threat. Traditional vulnerability detection methods rely heavily on expert-defined complex rules that are labor-int...
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An increased number of vehicles on the roads increases the risk of accidents and eventually resulted huge loss of lives and property. Accident detection utilizing computer vision in much consideration as of late. The ...
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People with severe upper limb disabilities need assistance from caregivers when eating, which becomes a burden for caregivers. Our laboratory has developed meal assistance robots that use image recognition and voice r...
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The Resonant Object Interface (ROI) is an acoustic input system that provides nuanced, embodied, and expressive engagement with control signals in audio software. The ROI’s unique sensing methodology is built from re...
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In the ever-expanding realm of wildlife conservation and ecological research, the use of automated image classification software has emerged as a valuable tool for extracting crucial insights from camera trap images. ...
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Sign language is a vital communication medium enabling individuals with speech and hearing impairments to convey thoughts, feelings, and ideas effectively. However, the limited understanding of sign language, outside ...
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Log parsing is defined as the process of extracting structured information from unstructured log data. It is an important step prior to many log analytics tasks. The emergence of Large Language Models (LLMs), like Gen...
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ISBN:
(纸本)9798350395693;9798350395686
Log parsing is defined as the process of extracting structured information from unstructured log data. It is an important step prior to many log analytics tasks. The emergence of Large Language Models (LLMs), like Generative Pre-trained Transformers (GPTs), has driven the development of novel log parsing methods. Existing studies have examined the effectiveness of large-scale general-purpose LLMs in log parsing. In this paper, we argue that the long-term adoption of such LLMs pose challenges of data privacy, cost, and tool integration. To address these challenges, we explore the viability of supervised fine-tuning of an open-source compact LLM for log parsing as a prospective alternative. To this end, we fine-tune the Mistral-7B-Instruct LLM on a diverse set of log files and evaluate its performance, in terms of both accuracy and robustness, against OpenAI's GPT-4-Turbo using different configuration settings. We apply two evaluation approaches, namely metric-based and LLM-based. Our overall findings show that fine-tuning a compact LLM such as Mistral-7B provides similar and sometimes better results than using a large-scale LLM, in our case GPT-4-Turbo. These findings are important because they enable companies to use a smaller LLM that they can readily adapt to parsing their log data, and integrate into their log analytics tools, without the need to rely on third-party LLM providers.
Based on the security of distributed machine learning data and model based on remote proof, a cross-domain dynamic remote proof scheme based on ring signature improves the efficiency of computer cluster proof, the app...
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