Ecosyste.ms: Awesome
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Projects in Awesome Lists by mattweingarten
A curated list of projects in awesome lists by mattweingarten .
https://github.com/mattweingarten/amazonecho
Alexaskills made simple for ruby!
alexa alexa-skill alexa-skills-kit amazon amazon-echo ruby ruby-gem ruby-on-rails rubygem rubygems
Last synced: 22 Dec 2024
https://github.com/mattweingarten/lambdapure
bachelor thesis: SSA IR for strict functional language
Last synced: 03 Jan 2025
https://github.com/mattweingarten/advanced-operating-systems
Adanced OS project, based on Barrelfish
Last synced: 03 Jan 2025
https://github.com/mattweingarten/avacare
Healthcare made easy with AvaCare!
Last synced: 03 Jan 2025
https://github.com/mattweingarten/bit-parallel-database-queries
high-performance SQL queries on bit-parallel & in memory database layouts
Last synced: 03 Jan 2025
https://github.com/mattweingarten/mlprojects
Machine learning projects for Introductory class
Last synced: 03 Jan 2025
https://github.com/mattweingarten/compileroptimzationsfuzzing
The automated software testing technique fuzzing has seen a golden age in the last decade, with widespread use in industry and academia. On the hunt to find vulnerabilities, fuzzing binaries are compiled with default compiler optimizations such as -O2, or -O3, which remain the hard-coded default in popular fuzzers such as AFL++. On a binary level, software compiled from the same source code may vastly differ in control flow depending on used compilation flags. In this work, we aim to analyze the impact of different compiler optimizations on the fuzzing process and provide further insight. We influence compilation passes of the clang/LLVM compiler and analyze their impact on the fuzzing performance of AFL++. We integrate our work into Fuzzbench, an open-source fuzzing pipeline, and run experiments on real-world benchmarks. Our preliminary fuzzing results indicate that there is a delicate trade-off between runtime performance and code complexity. While our results show significant differences on the scale of individual benchmarks, when summarizing across the whole bench suite, there is no evidence to suggest a statistical difference in fuzzing performance.
Last synced: 03 Jan 2025
https://github.com/mattweingarten/daem
Highly accurate Recommender Systems, including Collaborative Filtering, lie at the heart of a satisfactory customer experience and continuous user engagement for a plethora of large-scale online platforms. While Matrix Factorization is the most widely studied and applied Collaborative Filtering approach, there is evidence to suggest that linear techniques lack the complexity to sufficiently capture the underlying relationship between users and items. The use of neural networks like Autoencoders offers a potential remedy and may more accurately represent this relationship. In this work, we propose our Denoising Autoencoder Model (DÆM) for highly accurate Collaborative Filtering and show improvement over four evaluated state-of-the-art models.
Last synced: 03 Jan 2025
https://github.com/mattweingarten/riscv-pmu-core
Main repo for RISC-V project class EE6894
Last synced: 03 Jan 2025