COVALENT DRUG BINDING: A MECHANISTIC EXPLORATION TO ENHANCE SAFETY AND EFFICACY

Ph.DDOCTOR OF PHILOSOPH

A Novel Evaluation Framework for Assessing Resilience Against Prompt Injection Attacks in Large Language Models

Prompt injection attacks exploit vulnerabilities in large language models (LLMs) to manipulate the model into unintended actions or generate malicious content. As LLM integrated applications gain wider adoption, they face growing susceptibility to such attacks. This study introduces a novel evaluation framework for quantifying the resilience of applications. The framework incorporates innovative techniques designed to ensure representativeness, interpretability, and robustness. To ensure the representativeness of simulated attacks on the application, a meticulous selection process was employed, resulting in 115 carefully chosen attacks based on coverage and relevance. For enhanced interpretability, a second LLM was utilized to evaluate the responses generated from these simulated attacks. Unlike conventional malicious content classifiers that provide only a confidence score, the LLM-based evaluation produces a score accompanied by an explanation, thereby enhancing interpretability. Subsequently, a resilience score is computed by assigning higher weights to attacks with greater impact, thus providing a robust measurement of the application resilience. To assess the framework's efficacy, it was applied on two LLMs, namely Llama2 and ChatGLM. Results revealed that Llama2, the newer model exhibited higher resilience compared to ChatGLM. This finding substantiates the effectiveness of the framework, aligning with the prevailing notion that newer models tend to possess greater resilience. Moreover, the framework exhibited exceptional versatility, requiring only minimal adjustments to accommodate emerging attack techniques and classifications, thereby establishing itself as an effective and practical solution. Overall, the framework offers valuable insights that empower organizations to make well-informed decisions to fortify their applications against potential threats from prompt injection.Comment: Accepted to be published in the Proceedings of The 10th IEEE CSDE 2023, the Asia-Pacific Conference on Computer Science and Data Engineering 202

Detection and Defense Against Prominent Attacks on Preconditioned LLM-Integrated Virtual Assistants

The emergence of LLM (Large Language Model) integrated virtual assistants has brought about a rapid transformation in communication dynamics. During virtual assistant development, some developers prefer to leverage the system message, also known as an initial prompt or custom prompt, for preconditioning purposes. However, it is important to recognize that an excessive reliance on this functionality raises the risk of manipulation by malicious actors who can exploit it with carefully crafted prompts. Such malicious manipulation poses a significant threat, potentially compromising the accuracy and reliability of the virtual assistant's responses. Consequently, safeguarding the virtual assistants with detection and defense mechanisms becomes of paramount importance to ensure their safety and integrity. In this study, we explored three detection and defense mechanisms aimed at countering attacks that target the system message. These mechanisms include inserting a reference key, utilizing an LLM evaluator, and implementing a Self-Reminder. To showcase the efficacy of these mechanisms, they were tested against prominent attack techniques. Our findings demonstrate that the investigated mechanisms are capable of accurately identifying and counteracting the attacks. The effectiveness of these mechanisms underscores their potential in safeguarding the integrity and reliability of virtual assistants, reinforcing the importance of their implementation in real-world scenarios. By prioritizing the security of virtual assistants, organizations can maintain user trust, preserve the integrity of the application, and uphold the high standards expected in this era of transformative technologies.Comment: Accepted to be published in the Proceedings of the 10th IEEE CSDE 2023, the Asia-Pacific Conference on Computer Science and Data Engineering 202

Addressing quality, access and equity in the school direct subsidy scheme in Hong Kong : a study of government strategies and tools

published_or_final_versionPolitics and Public AdministrationMasterMaster of Public Administratio

Friction stir processing of aluminium-silicon alloys

Friction Stir Processing (FSP) has the potential for locally enhancing the properties of Al-Si alloy castings, for demanding applications within the automotive industry. In this thesis, the effect of FSP has been examined on three different cast Al-Si alloys:i) A Hypoeutectic Al-8.9wt%Si Alloyii) A Hypereutectic Al-12.1wt%Si Alloyiii) A Hypereutectic Al-12.1wt%Si-2.4wt%Ni AlloyThe influence of different processing parameters has been investigated at a fundamental level. Image analysis of particle size distributions and growth method of tessellation were used to quantify the level of particle refinement and the homogeneity of the second phase spatial distribution. Stop-action experiments were also carried out, to allow the microstructural changes around the tool during FSP to be studied. Two computer models have been explored, in order to predict the temperature distribution and the material flow behaviour. Furthermore, the stability of the microstructure of the friction stir processed material was studied after being heat treated at elevated temperatures. The changes in particle size and grain structure were examined, hardness measurements were taken across the PZ, and tensile testing were carried out at room and elevated temperatures.After FSP, the microstructure of the cast Al-Si alloys was greatly refined. However, differences in microstructure have been observed throughout the PZ, which tended to be better refined and distributed on the advancing side, than the retreating side of the PZ. Changing the processing parameters also influenced the size and spatial distribution of the second phase particles. By studying the changes in microstructure around the tool from the stop-action experiments, and comparing the results to the thermal distribution and material flow behaviour predicted by the computer models, it has been shown that the flow stress, pitch, and temperature of processing, all needed to be considered, when determining the effects that FSP have on the microstructure. FSP caused very little changes to the hardness of the material, while tensile properties were greatly improved, due to the elimination of porosity and refinement of large flawed particles. In terms of the stability of the microstructure after FSP, particle coarsening and abnormal grain growth has been observed during high temperatures heat treatment. Furthermore, the Al2Cu phase was found to dissolve into solid solution at elevated temperatures, so GPZs and solute clustering can then develop within the alloy during natural ageing.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

The role of linked building data (LBD) in aligning augmented reality (AR) with sustainable construction

Over the years, the construction industry has been evolving to embrace the delicate balance between buildings and a sustainable environment by optimizing resource use to create greener and more energy efficient constructions. Sustainable building design and optimization is a highly iterative and complicated process. This is mainly attributed to the complex interaction between the different heterogenous but heuristic construction processes, building systems and workflows involved in achieving this goal. Augmented Reality (AR) has rapidly emerged as a revolutionary technology that could play a key role towards improving coordination of sustainable design processes. AR makes possible the real-time visualization of a three-dimensional (3D) building prototype with linked design information in a real-world environment based on a two-dimensional drawing. From past research, it is evident that this technology relies heavily on a common data environment (CDE) that syncs all construction processes with their related building information in one central model. However, due to the fragmented nature of the construction industry, different domain experts generate and exchange vast amounts of heterogenous information using different software tools outside a CDE. This paper therefore investigates the performance gap that exists within Malaysia’s construction industry towards using linked building data (LBD) with AR to improve the lifecycle sustainability of buildings. The results of this study clearly delineate how current construction practices in Malaysia do not favor the use of AR however, stakeholder perception is positive towards adoption of workflows that link heterogenous building data to streamline AR with sustainable building design and construction

OMMA enables population-scale analysis of complex genomic features and phylogenomic relationships from nanochannel-based optical maps.

BackgroundOptical mapping is an emerging technology that complements sequencing-based methods in genome analysis. It is widely used in improving genome assemblies and detecting structural variations by providing information over much longer (up to 1 Mb) reads. Current standards in optical mapping analysis involve assembling optical maps into contigs and aligning them to a reference, which is limited to pairwise comparison and becomes bias-prone when analyzing multiple samples.FindingsWe present a new method, OMMA, that extends optical mapping to the study of complex genomic features by simultaneously interrogating optical maps across many samples in a reference-independent manner. OMMA captures and characterizes complex genomic features, e.g., multiple haplotypes, copy number variations, and subtelomeric structures when applied to 154 human samples across the 26 populations sequenced in the 1000 Genomes Project. For small genomes such as pathogenic bacteria, OMMA accurately reconstructs the phylogenomic relationships and identifies functional elements across 21 Acinetobacter baumannii strains.ConclusionsWith the increasing data throughput of optical mapping system, the use of this technology in comparative genome analysis across many samples will become feasible. OMMA is a timely solution that can address such computational need. The OMMA software is available at https://github.com/TF-Chan-Lab/OMTools

Roles of the CHADS2 and CHA2DS2-VASc scores in post-myocardial infarction patients: Risk of new occurrence of atrial fibrillation and ischemic stroke

Background: Patients with myocardial infarction (MI) are at risk of the development of atrial fibrillation (AF) and ischemic stroke. We sought to evaluate the prognostic performance of the CHADS2 and CHA2DS2-VASc scores in predicting new AF and/or ischemic stroke in post-ST segment elevation MI (STEMI) patients. Six hundred and seven consecutive post-STEMI patients with no previously documented AF were studied.Methods and Results: After a follow-up of 63 months (3,184 patient-years), 83 (13.7%) patients developed new AF (2.8% per year). Patients with a high CHADS2 and/or CHA2DS2-VASc score were more likely to develop new AF. The annual incidence of new AF was 1.18%, 2.10%, 4.52%, and 7.03% in patients with CHADS2 of 0, 1, 2, and ≥ 3; and 0.39%, 1.72%, 1.83%, and 5.83% in patients with a CHA2DS2-VASc score of 1, 2, 3 and ≥ 4. The CHA2DS2-VASc score (C-statistic = 0.676) was superior to the CHADS2 (C-statistic = 0.632) for discriminating new AF. Ischemic stroke occurred in 29 patients (0.9% per year), the incidence increasing in line with the CHADS2 (0.41%, 1.02%, 1.11%, and 1.95% with score of 0, 1, 2, and ≥ 3) and CHA2DS2-VASc scores (0.39%, 0.49%, 1.02%, and 1.48% with score of 1, 2, 3 and ≥ 4). The C-statistic of the CHA2DS2-VASc score as a predictor of ischemic stroke was 0.601, superior to that of CHADS2 score (0.573). CHADS2 and CHA2DS2-VASc scores can identify post-STEMI patients at high risk of AF and stroke.Conclusions: The CHADS2 and CHA2DS2-VASc scores can identify post-STEMI patients at high risk of AF and ischemic stroke. This enables close surveillance and prompt anticoagulation for stroke prevention