title: An Interactive User Management System for Multilingual Documents: A case study of the Pei Jones collection

authors: Cader, Papitha

subjects: Greenstone Digital Library

abstract: Can general purpose DL software architecture be designed to provide a flexible environment that supports both the management and end-user roles through a single unified web interface? In particular we take the collected correspondence of the Māori scholar Dr. Pei Jones and undertake case study of how this approach can be applied in a practical way.

text: An Interactive User Management System for Multilingual Documents: A case study of the Pei Jones collection Can general purpose DL software architecture be designed to provide a flexible environment that supports both the management and end-user roles through a single unified web interface? In particular we take the collected correspondence of the Māori scholar Dr. Pei Jones and undertake case study of how this approach can be applied in a practical way.

year: 2012

title: Te Arawa– Kaitaki of Te Arawa Lakes

authors: Curtis, Sir Toby

abstract: The Te Arawa Lakes Trust has embarked on a project to develop a Te Arawa Cultural Values Framework, in regard to the 14 lakes located in our tribal region. There are a host of positives that we believe can accrue as an outcome of this exercise. Already the calibre of people acknowledged for their cultural expertise have added a lot by their attendance. Their presence and contribution injected a level of enthusiasm and confidence in the compilation of the final draft. It is hoped that distribution among the wider community will elicit constructive feedback.

text: Te Arawa– Kaitaki of Te Arawa Lakes The Te Arawa Lakes Trust has embarked on a project to develop a Te Arawa Cultural Values Framework, in regard to the 14 lakes located in our tribal region. There are a host of positives that we believe can accrue as an outcome of this exercise. Already the calibre of people acknowledged for their cultural expertise have added a lot by their attendance. Their presence and contribution injected a level of enthusiasm and confidence in the compilation of the final draft. It is hoped that distribution among the wider community will elicit constructive feedback.

year: 2015

title: Analysing and scoring cybersecurity vulnerability reports using deep learning

authors: Zhang, Zijing

abstract: Vulnerability analysis plays a crucial role in cybersecurity, as it helps identify and mitigate potential threats to systems and networks. It is essential for organizations to understand the severity of vulnerabilities and prioritize their remediation efforts. This thesis focuses on the analysis and scoring of cybersecurity vulnerability reports using deep learning techniques. By leveraging large language models and machine learning algorithms, this thesis aims to improve the accuracy and efficiency of vulnerability classification and severity assessment.  

The research presented in this thesis includes the development of models to estimate the CVSS of vulnerabilities based on their descriptions, comparing the performance of AI models with human experts. For CVSS 3.1 severity level prediction task, the USE model achieves an accuracy of 0.73. The USE model outperforms human experts with a macro mean squared error of 0.128, which is four times lower than that of human experts (0.537). Meanwhile, quality of vulnerability descriptions is also analyzed to understand its impact on the performance of machine learning models. A BERT-based model (‘DesQ’) is proposed to assess the quality of vulnerability descriptions, achieving an accuracy of 0.76. A dataset of vulnerability descriptions with quality labels is curated and analyzed to understand the distribution of description quality across different vendors and reporting agencies. The analyzer BART model (‘EVAL’) identifies missing essential vulnerability aspects. Trained on a specialized quality dataset of 450 manually labeled entries, EVAL identifies Vulnerability Impact as the most frequently missing component across analyzed weaknesses. This thesis also explores the use of explainable AI techniques to provide insights into the decision-making process of machine learning models, enhancing their interpretability and trustworthiness in mapping root causes to vulnerabilities.

text: Analysing and scoring cybersecurity vulnerability reports using deep learning Vulnerability analysis plays a crucial role in cybersecurity, as it helps identify and mitigate potential threats to systems and networks. It is essential for organizations to understand the severity of vulnerabilities and prioritize their remediation efforts. This thesis focuses on the analysis and scoring of cybersecurity vulnerability reports using deep learning techniques. By leveraging large language models and machine learning algorithms, this thesis aims to improve the accuracy and efficiency of vulnerability classification and severity assessment.  

The research presented in this thesis includes the development of models to estimate the CVSS of vulnerabilities based on their descriptions, comparing the performance of AI models with human experts. For CVSS 3.1 severity level prediction task, the USE model achieves an accuracy of 0.73. The USE model outperforms human experts with a macro mean squared error of 0.128, which is four times lower than that of human experts (0.537). Meanwhile, quality of vulnerability descriptions is also analyzed to understand its impact on the performance of machine learning models. A BERT-based model (‘DesQ’) is proposed to assess the quality of vulnerability descriptions, achieving an accuracy of 0.76. A dataset of vulnerability descriptions with quality labels is curated and analyzed to understand the distribution of description quality across different vendors and reporting agencies. The analyzer BART model (‘EVAL’) identifies missing essential vulnerability aspects. Trained on a specialized quality dataset of 450 manually labeled entries, EVAL identifies Vulnerability Impact as the most frequently missing component across analyzed weaknesses. This thesis also explores the use of explainable AI techniques to provide insights into the decision-making process of machine learning models, enhancing their interpretability and trustworthiness in mapping root causes to vulnerabilities.

year: 2026