Deep Learning for Time Series Classification
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Updated
Mar 18, 2023 - Python
Deep Learning for Time Series Classification
Python framework for automatically executing measurement-based experiments on native and web apps running on Android devices
Artifact repository for the paper "Lost in Translation: A Study of Bugs Introduced by Large Language Models while Translating Code", In Proceedings of The 46th IEEE/ACM International Conference on Software Engineering (ICSE 2024), Lisbon, Portugal, April 2024
一个基于中国市场的Fama-French五因子实证研究
一个基于中国市场的BW投资者情绪指标实证研究
Codes to clean data and construct variables for empirical finance.
The repository contains code and data for the paper https://github.com/sumonbis/ML-Fairness/blob/master/ml-fairness.pdf, to be appeared at ESEC/FSE 2020.
This is repository contains code for experiment to evaluate catastrophic forgetting in neural networks.
An experiment to demonstrate the biases and predictability of our world.
Sequential testing for efficient and reliable comparison of stochastic algorithms.
Investigate BERT on Non-linearity and Layer Commutativity
Python script, which creates images from code for program-comprehension studies.
Replication package for the ICSE-SEIS 2024 paper titled "An Empirical Study on Compliance with Ranking Transparency in the Software Documentation of EU Online Platforms".
Extract and aggregate tables of empirical results from computer science papers
📄 Replication Package: From Gender Biases to Gender-Inclusive Design: An Empirical Investigation
Reproduction package for the paper "A Large-scale Study of Security Vulnerability Support on Developer Q&A Websites" in the Evaluation and Assessment in Software Engineering (EASE) 2021
Framework to compare commit-untangling tools fairly and accurately.
Global empirical models for tropopause height determination
This script formats program code in a way that the Presentation software displays it with configurable syntax highlighting.
EASE 2022 - An empirical study on the Performance and Energy Consumption of AI Containerization Strategies for Computer-Vision Tasks
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