Understanding and Optimizing Python-Based Applications - A Case Study on PYPY

Python is nowadays one of the most popular programming languages. It has been used extensively for rapid prototyping and developing real-world applications. Unfortunately, very few empirical studies were conducted on Python-based applications. There are various Python implementations (e.g., CPython, and PyPy). Among them, PyPy is generally the fastest due to PyPy's efficient tracing-based Just-in-Time (JIT) compiler. Understanding how PyPy has been evolved and the rationale behind its high performance would be very useful for Python application developers and researchers. In the first part of the thesis, we conducted a replication study on mining the historical code changes' of PyPy and compared our findings against Python-based applications from five other application domains. In the second part, we conducted a detailed empirical study on the performance impact of the JIT configuration settings of PyPy. The findings and the techniques in this thesis will be useful for Python application developers and researchers

Leakage-resilient biometric-based remote user authentication with fuzzy extractors

National Research Foundation (NRF) Singapor

Privacy-preserving communication and power injection over vehicle networks and 5G smart grid slice

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Editing-enabled signatures: A new tool for editing authenticated data

tru

Neighbor Regularized Bayesian Optimization for Hyperparameter Optimization

Bayesian Optimization (BO) is a common solution to search optimal hyperparameters based on sample observations of a machine learning model. Existing BO algorithms could converge slowly even collapse when the potential observation noise misdirects the optimization. In this paper, we propose a novel BO algorithm called Neighbor Regularized Bayesian Optimization (NRBO) to solve the problem. We first propose a neighbor-based regularization to smooth each sample observation, which could reduce the observation noise efficiently without any extra training cost. Since the neighbor regularization highly depends on the sample density of a neighbor area, we further design a density-based acquisition function to adjust the acquisition reward and obtain more stable statistics. In addition, we design a adjustment mechanism to ensure the framework maintains a reasonable regularization strength and density reward conditioned on remaining computation resources. We conduct experiments on the bayesmark benchmark and important computer vision benchmarks such as ImageNet and COCO. Extensive experiments demonstrate the effectiveness of NRBO and it consistently outperforms other state-of-the-art methods.Comment: Accepted by BMVC 202

A new framework for privacy-preserving biometric-based remote user authentication

tru

A new construction for linkable secret handshake

National Research Foundation (NRF) Singapore; AXA Research Fun

PriBioAuth: Privacy-preserving biometric-based remote user authentication

National Research Foundation (NRF) Singapor

Text-to-3D with Classifier Score Distillation

Text-to-3D generation has made remarkable progress recently, particularly with methods based on Score Distillation Sampling (SDS) that leverages pre-trained 2D diffusion models. While the usage of classifier-free guidance is well acknowledged to be crucial for successful optimization, it is considered an auxiliary trick rather than the most essential component. In this paper, we re-evaluate the role of classifier-free guidance in score distillation and discover a surprising finding: the guidance alone is enough for effective text-to-3D generation tasks. We name this method Classifier Score Distillation (CSD), which can be interpreted as using an implicit classification model for generation. This new perspective reveals new insights for understanding existing techniques. We validate the effectiveness of CSD across a variety of text-to-3D tasks including shape generation, texture synthesis, and shape editing, achieving results superior to those of state-of-the-art methods. Our project page is https://xinyu-andy.github.io/Classifier-Score-DistillationComment: Our project page is https://xinyu-andy.github.io/Classifier-Score-Distillatio