Computer Science
http://hdl.handle.net/10012/9930
2024-03-29T09:22:41ZCardinality Estimation in Streaming Graph Data Management Systems
http://hdl.handle.net/10012/20366
Cardinality Estimation in Streaming Graph Data Management Systems
Akillioglu, Kerem
Graph processing has become an increasingly popular paradigm for data management
systems. Concurrently, there is a pronounced demand for specialized systems dedicated
to streaming processing that are essential to address the continual flow of data and the
inherent dynamism in streaming data. Yet, the lack of a standardized, general-purpose
query framework specifically for streaming graphs is a notable gap in existing technologies.
This shortfall emphasizes the necessity for a more comprehensive solution for processing
and analyzing streaming graph data efficiently in real time. Enhancing this solution is
crucially dependent on improving the query processing pipeline, especially on cardinality
estimation and query optimization, both of which are key factors in ensuring optimal
system performance.
In this thesis, a novel cardinality estimation technique, called GraphSketch, that
is tailored for streaming graph database management systems (GDBMS) is proposed.
GraphSketch is a sketch-based framework designed to concisely summarize streaming
graphs, enabling both accurate and efficient cardinality estimations. The thesis delves
into the theoretical foundations of GraphSketch, outlining its conceptual design and the
specific methodologies employed in its construction. Additionally, the thesis elaborates
on the suitability of GraphSketch for streaming systems, highlighting its capability for
incremental updates, which are pivotal in maintaining efficiency in the rapidly evolving
environment of streaming data.
2024-02-23T00:00:00ZMS/MS Spectrum Prediction for MHC-Associated Peptides with a Fine-Tuned Model
http://hdl.handle.net/10012/20364
MS/MS Spectrum Prediction for MHC-Associated Peptides with a Fine-Tuned Model
Li, Zhenbo
To improve the quality of spectral library search, several MS/MS spectrum predictors have been developed in the last decades. After success in various fields, deep learning techniques are adopted by MS/MS spectrum predictors to increase the accuracy of predicted spectra. However, the quality and quantity of the training set are both required to train a deep learning model. Due to the less representation of MHC-associated peptides in most spectral libraries, current MS/MS spectrum predictors provide less accurate predicted spectra for MHC-associated peptides than their performance for other peptides.
In this thesis, we built several MHC-associated peptide spectral libraries for training and evaluation purposes. We selected PredFull as our base model and performed transfer learning with these MHC-associated peptide libraries, which are much smaller than com- mon tryptic spectral libraries. The result showed that the fine-tuned model outperformed the original model significantly when predicting MHC-associated peptides.
2024-02-23T00:00:00ZAnalyzing Threats of Large-Scale Machine Learning Systems
http://hdl.handle.net/10012/20355
Analyzing Threats of Large-Scale Machine Learning Systems
Lukas, Nils
Large-scale machine learning systems such as ChatGPT rapidly transform how we interact with and trust digital media. However, the emergence of such a powerful technology faces a dual-use dilemma. While it can have many positive societal impacts in providing equitable access to information, ML systems can also be misused by untrustworthy entities to cause intentional harm. For example, a system could unintentionally disclose private information about its training data and jeopardize the privacy of individuals in the training data. The system's generated content could also be misused for unethical purposes, such as eroding trust in digital media by misrepresenting generating content as authentic. Providing untrustworthy users with these new capabilities could amplify potential negative consequences emerging through this technology, such as a proliferation of deep fakes or disinformation. I analyze these threats from two perspectives: (i) Data leakage, when the model cannot be trusted because it has memorized private information during training, and (ii) Misuse when users cannot be trusted to use the system for its intended purposes. This thesis presents five projects to assess these risks to the privacy and security of ML systems and evaluates the reliability of known countermeasures. To do so, I assess the privacy risks of extracting Personally Identifiable Information from language models trained with differential privacy. As a method of controlling unintended use, I study the effectiveness and robustness of fingerprinting and watermarking methods to detect the provenance of models and their generated content.
2024-02-22T00:00:00ZTraffic Rule Checking and Validation
http://hdl.handle.net/10012/20344
Traffic Rule Checking and Validation
Stewart, Connor
This thesis presents a comprehensive exploration of traffic rule verification systems for diverse junction types, addressing key challenges in formalizing rules, determining violation thresholds, and covering a wide spectrum of relevant traffic scenarios. Leveraging iterative implementations and extensions of existing approaches, the associated program aims to concretize literature-based methods, and understand the severity of rule violations in naturalistic driving. The study extensively tests traffic rule adherence by vehicles in simulated and recorded traffic, utilizing Lanelet2, a versatile mapping system, to cover both signalized and non-signalized stop-regulated intersections. Through statistical analyses, the research delivers results on rule-violation thresholds, associated coefficients, and traffic violation rates, encompassing scenarios such as stop sign compliance, turns after stops, traffic light violations, offroad occurrences, speed limit violations, and tailgating instances. The thesis contributes specific test cases and insights from naturalistic driving, showcasing parameter settings and threshold determination for effective traffic rule implementation. The comprehensive approach taken in this research contributes to the advancement of traffic rule verification systems and provides a foundation for evaluating autonomous vehicle behaviours in diverse junction scenarios.
2024-02-15T00:00:00Z