no code implementations • CCL 2021 • Dongzhen Wen, Fan Zhang, Xiao Zhang, Liang Yang, Yuan Lin, Bo Xu, Hongfei Lin
“软件源代码的理解则是软件协同开发与维护的核心, 而源代码中占半数以上的标识符的理解则在软件理解中起到重要作用, 传统软件工程主要研究通过命名规范限制标识符的命名过程以构造更易理解和交流的标识符。本文则在梳理分析常见编程语言命名规范的基础上, 提出一种全新的标识符可理解性评价标准。具体而言, 本文首先总结梳理了常见主流编程语言中的命名规范并类比自然语言语素概念本文提出基于软件语素的标识符构成过程, 即标识符的构成可被视为软件语素的生成、排列和连接过程。在此基础上, 本文提出一种结合自然语料库的软件标识符规范性评价方法, 用来衡量软件标识符是否易于理解。最后, 本文通过源代码理解数据集和乇乩乴乨乵乢平台中开源项目对规范性指标进行了验证性实验, 结果表明本文提出的规范性分数能够很好衡量软件项目的可理解性。”
no code implementations • 23 May 2024 • Peiyuan Feng, Yichen He, Guanhua Huang, Yuan Lin, Hanchong Zhang, Yuchen Zhang, Hang Li
Our extensive experiments on ProductQA and MedMCQA show that AGILE agents based on 13B and 7B LLMs trained with PPO can outperform GPT-4 agents.
no code implementations • 5 Mar 2024 • Yuan Lin, Antai Xie, Xiao Liu
Most of the current studies on autonomous vehicle decision-making and control tasks based on reinforcement learning are conducted in simulated environments.
no code implementations • 1 Mar 2024 • Ruichen Xu, Xiao Liu, Jinming Xu, Yuan Lin
We introduce safe hybrid-action reinforcement learning into discretionary lane change for the first time and propose Parameterized Soft Actor-Critic with PID Lagrangian (PASAC-PIDLag) algorithm.
no code implementations • 27 Feb 2024 • Zishun Zheng, Yihan Wang, Yuan Lin
This approach divides the driving control of vehicles on highways into two parts: lane-change decision and lane-change control, both of which are solved using the MPC method.
no code implementations • 14 Feb 2024 • Liyao Wang, Zishun Zheng, Yuan Lin
The selection of a reward function in Reinforcement Learning (RL) has garnered significant attention because of its impact on system performance.
1 code implementation • 25 Nov 2023 • Xiuyuan Chen, Yuan Lin, Yuchen Zhang, Weiran Huang
By using instance-specific rules as prompt, GPT-4, as an automatic evaluator, can achieve a stable evaluation accuracy of around 97. 0\%, comparable to the 94. 9\% - 97. 5\% accuracy of a human evaluator.
1 code implementation • 2 Nov 2023 • Xiaokun Zhang, Bo Xu, Fenglong Ma, Chenliang Li, Yuan Lin, Hongfei Lin
Secondly, price preference and interest preference are interdependent and collectively determine user choice, necessitating that we jointly consider both price and interest preference for intent modeling.
no code implementations • 15 Jun 2023 • Changfu Gong, Jinming Xu, Yuan Lin
The energy management of certain series-parallel PHEVs involves the control of continuous variables, such as engine torque, and discrete variables, such as clutch engagement/disengagement.
1 code implementation • 2 May 2023 • Jinming Xu, Yuan Lin
Such problems are usually formulated as mixed-integer optimal control (MIOC) problems, which are challenging to solve due to the complexity of the solution space.
no code implementations • 2 Mar 2023 • Qitao Li, Changfu Gong, Yuan Lin
In this paper, a model predictive mixed integer control method for BYD Qin Plus DM-i (Dual Model intelligent) plug-in hybrid electric vehicle (PHEV) is proposed for co-optimization to reduce fuel consumption during car following.
no code implementations • 30 Jan 2023 • Junyong You, Yuan Lin, Jari Korhonen
Deep networks have demonstrated promising results in the field of Image Quality Assessment (IQA).
no code implementations • 6 Sep 2022 • Michael Yang, Yuan Lin, Chiuman Ho
The existing Optical Character Recognition (OCR) systems are capable of recognizing images with horizontal texts.
Optical Character Recognition Optical Character Recognition (OCR)
2 code implementations • 20 Mar 2022 • Zhixuan Liu, ZiHao Wang, Yuan Lin, Hang Li
Deep neural networks, empowered by pre-trained language models, have achieved remarkable results in natural language understanding (NLU) tasks.
no code implementations • 7 Mar 2022 • Yuan Lin, John McPhee, Nasser L. Azad
Current research on Deep Reinforcement Learning (DRL) for automated on-ramp merging neglects vehicle powertrain and dynamics.
no code implementations • 26 May 2021 • Jiachen Li, Yuan Lin, Rongrong Liu, Chiu Man Ho, Humphrey Shi
Segmentation-based scene text detection methods have been widely adopted for arbitrary-shaped text detection recently, since they make accurate pixel-level predictions on curved text instances and can facilitate real-time inference without time-consuming processing on anchors.