arXiv subjects

Anj Simmons

Publications and source records attributed to Anj Simmons.

11 recordsLinked to original sources

arXiv2024

Minimising changes to audit when updating decision trees

Interpretable models are important, but what happens when the model is updated on new training data? We propose an algorithm for updating a decision tree while minimising the number of changes to the tree that a human would need to audit. We achieve this via a greedy approach that incorporates the number of changes to the tree as part of the objective function. We compare our algorithm to existing methods and show that it sits in a sweet spot between final accuracy and number of changes to audit.

cs.LG↗

arXiv2024

Quantifying Manifolds: Do the manifolds learned by Generative Adversarial Networks converge to the real data manifold

This paper presents our experiments to quantify the manifolds learned by ML models (in our experiment, we use a GAN model) as they train. We compare the manifolds learned at each epoch to the real manifolds representing the real data. To quantify a manifold, we study the intrinsic dimensions and topological features of the manifold learned by the ML model, how these metrics change as we continue to train the model, and whether these metrics convergence over the course of training to the metrics of the real data manifold.

cs.LG↗

arXiv2023

Detecting out-of-distribution text using topological features of transformer-based language models

To safeguard machine learning systems that operate on textual data against out-of-distribution (OOD) inputs that could cause unpredictable behaviour, we explore the use of topological features of self-attention maps from transformer-based language models to detect when input text is out of distribution. Self-attention forms the core of transformer-based language models, dynamically assigning vectors to words based on context, thus in theory our methodology is applicable to any transformer-based language model with multihead self-attention. We evaluate our approach on BERT and compare it to a traditional OOD approach using CLS embeddings. Our results show that our approach outperforms CLS embeddings in distinguishing in-distribution samples from far-out-of-domain samples, but struggles with near or same-domain datasets.

cs.CL↗

arXiv2023

MLGuard: Defend Your Machine Learning Model!

Machine Learning (ML) is used in critical highly regulated and high-stakes fields such as finance, medicine, and transportation. The correctness of these ML applications is important for human safety and economic benefit. Progress has been made on improving ML testing and monitoring of ML. However, these approaches do not provide i) pre/post conditions to handle uncertainty, ii) defining corrective actions based on probabilistic outcomes, or iii) continual verification during system operation. In this paper, we propose MLGuard, a new approach to specify contracts for ML applications. Our approach consists of a) an ML contract specification defining pre/post conditions, invariants, and altering behaviours, b) generated validation models to determine the probability of contract violation, and c) an ML wrapper generator to enforce the contract and respond to violations. Our work is intended to provide the overarching framework required for building ML applications and monitoring their safety.

cs.SE↗

arXiv2023

Garbage in, garbage out: Zero-shot detection of crime using Large Language Models

This paper proposes exploiting the common sense knowledge learned by large language models to perform zero-shot reasoning about crimes given textual descriptions of surveillance videos. We show that when video is (manually) converted to high quality textual descriptions, large language models are capable of detecting and classifying crimes with state-of-the-art performance using only zero-shot reasoning. However, existing automated video-to-text approaches are unable to generate video descriptions of sufficient quality to support reasoning (garbage video descriptions into the large language model, garbage out).

cs.CL↗

arXiv2023

Green Runner: A tool for efficient model selection from model repositories

Deep learning models have become essential in software engineering, enabling intelligent features like image captioning and document generation. However, their popularity raises concerns about environmental impact and inefficient model selection. This paper introduces GreenRunnerGPT, a novel tool for efficiently selecting deep learning models based on specific use cases. It employs a large language model to suggest weights for quality indicators, optimizing resource utilization. The tool utilizes a multi-armed bandit framework to evaluate models against target datasets, considering tradeoffs. We demonstrate that GreenRunnerGPT is able to identify a model suited to a target use case without wasteful computations that would occur under a brute-force approach to model selection.

cs.SE↗

arXiv2022

Comparative analysis of real bugs in open-source Machine Learning projects -- A Registered Report

Background: Machine Learning (ML) systems rely on data to make predictions, the systems have many added components compared to traditional software systems such as the data processing pipeline, serving pipeline, and model training. Existing research on software maintenance has studied the issue-reporting needs and resolution process for different types of issues, such as performance and security issues. However, ML systems have specific classes of faults, and reporting ML issues requires domain-specific information. Because of the different characteristics between ML and traditional Software Engineering systems, we do not know to what extent the reporting needs are different, and to what extent these differences impact the issue resolution process. Objective: Our objective is to investigate whether there is a discrepancy in the distribution of resolution time between ML and non-ML issues and whether certain categories of ML issues require a longer time to resolve based on real issue reports in open-source applied ML projects. We further investigate the size of fix of ML issues and non-ML issues. Method: We extract issues reports, pull requests and code files in recent active applied ML projects from Github, and use an automatic approach to filter ML and non-ML issues. We manually label the issues using a known taxonomy of deep learning bugs. We measure the resolution time and size of fix of ML and non-ML issues on a controlled sample and compare the distributions for each category of issue.

cs.SE↗

arXiv2022

SignalKG: Towards Reasoning about the Underlying Causes of Sensor Observations

This paper demonstrates our vision for knowledge graphs that assist machines to reason about the cause of signals observed by sensors. We show how the approach allows for constructing smarter surveillance systems that reason about the most likely cause (e.g., an attacker breaking a window) of a signal rather than acting directly on the received signal without consideration for how it was produced.

cs.AI↗

arXiv2022

Signal Knowledge Graph

This paper presents an knowledge graph to assist in reasoning over signals for intelligence purposes. We highlight limitations of existing knowledge graphs and reasoning systems for this purpose, using inference of an attack using combined data from microphones, cameras and social media as an example. Rather than acting directly on the received signal, our approach considers attacker behaviour, signal emission, receiver characteristics, and how signals are summarised to support inferring the underlying cause of the signal.

cs.AI↗

arXiv2022

MLSmellHound: A Context-Aware Code Analysis Tool

Meeting the rise of industry demand to incorporate machine learning (ML) components into software systems requires interdisciplinary teams contributing to a shared code base. To maintain consistency, reduce defects and ensure maintainability, developers use code analysis tools to aid them in identifying defects and maintaining standards. With the inclusion of machine learning, tools must account for the cultural differences within the teams which manifests as multiple programming languages, and conflicting definitions and objectives. Existing tools fail to identify these cultural differences and are geared towards software engineering which reduces their adoption in ML projects. In our approach we attempt to resolve this problem by exploring the use of context which includes i) purpose of the source code, ii) technical domain, iii) problem domain, iv) team norms, v) operational environment, and vi) development lifecycle stage to provide contextualised error reporting for code analysis. To demonstrate our approach, we adapt Pylint as an example and apply a set of contextual transformations to the linting results based on the domain of individual project files under analysis. This allows for contextualised and meaningful error reporting for the end-user.

cs.SE↗

arXiv2022

A reliability measure for smart surveillance systems

We present a reliability measure for smart surveillance systems, taking into account the adversarial nature of intrusion. Our approach is based on percolation theory and is a generalisation of Hamedmoghadam et al.'s reliability measure. Specifically, our approach incorporates a customisable cost function to allow modelling a diverse range of situations, such as access restrictions, monitoring, and failures. We demonstrate our approach by applying it to a digital twin of a smart building. However, challenges remain in estimating and modelling the key parameters needed.

eess.SY↗