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Found 21 papers, 10 papers with code
软件标识符的自然语言规范性研究(Research on the Natural Language Normalness of Software Identifiers)
“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。”
PaSa: An LLM Agent for Comprehensive Academic Paper Search
Notably, PaSa-7B surpasses the best Google-based baseline, Google with GPT-4o, by 37. 78% in recall@20 and 39. 90% in recall@50.
Tarsier2: Advancing Large Vision-Language Models from Detailed Video Description to Comprehensive Video Understanding
We introduce Tarsier2, a state-of-the-art large vision-language model (LVLM) designed for generating detailed and accurate video descriptions, while also exhibiting superior general video understanding capabilities.
Ranked #2 on
Video Question Answering
on TVBench
AutoPatent: A Multi-Agent Framework for Automatic Patent Generation
As the capabilities of Large Language Models (LLMs) continue to advance, the field of patent processing has garnered increased attention within the natural language processing community.
StoryTeller: Improving Long Video Description through Global Audio-Visual Character Identification
We propose StoryTeller, a system for generating dense descriptions of long videos, incorporating both low-level visual concepts and high-level plot information.
IPEval: A Bilingual Intellectual Property Agency Consultation Evaluation Benchmark for Large Language Models
The rapid development of Large Language Models (LLMs) in vertical domains, including intellectual property (IP), lacks a specific evaluation benchmark for assessing their understanding, application, and reasoning abilities.
AGILE: A Novel Reinforcement Learning Framework of LLM Agents
Our ablation study highlights the indispensability of memory, tools, consultation, reflection, and reinforcement learning in achieving the agent's strong performance.
Autonomous vehicle decision and control through reinforcement learning with traffic flow randomization
Most of the current studies on autonomous vehicle decision-making and control tasks based on reinforcement learning are conducted in simulated environments.
Safe Hybrid-Action Reinforcement Learning-Based Decision and Control for Discretionary Lane Change
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.
Highway Discretionary Lane-change Decision and Control Using Model Predictive Control
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.
Steady-State Error Compensation for Reinforcement Learning with Quadratic Rewards
The selection of a reward function in Reinforcement Learning (RL) has garnered significant attention because of its impact on system performance.
AutoEval-Video: An Automatic Benchmark for Assessing Large Vision Language Models in Open-Ended Video Question Answering
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.
Bi-Preference Learning Heterogeneous Hypergraph Networks for Session-based Recommendation
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.
Plug-in Hybrid Electric Vehicle Energy Management with Clutch Engagement Control via Continuous-Discrete Reinforcement Learning
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.
Mixed-Integer Optimal Control via Reinforcement Learning: A Case Study on Hybrid Electric Vehicle Energy Management
Many optimal control problems require the simultaneous output of discrete and continuous control variables.
Co-Optimization of Adaptive Cruise Control and Hybrid Electric Vehicle Energy Management via Model Predictive Mixed Integer Control
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.
Half of an image is enough for quality assessment
Deep networks have demonstrated promising results in the field of Image Quality Assessment (IQA).
A Masked Bounding-Box Selection Based ResNet Predictor for Text Rotation Prediction
The existing Optical Character Recognition (OCR) systems are capable of recognizing images with horizontal texts.
Optical Character Recognition
Optical Character Recognition (OCR)
A Neural-Symbolic Approach to Natural Language Understanding
Deep neural networks, empowered by pre-trained language models, have achieved remarkable results in natural language understanding (NLU) tasks.
Co-Optimization of On-Ramp Merging and Plug-In Hybrid Electric Vehicle Power Split Using Deep Reinforcement Learning
Current research on Deep Reinforcement Learning (DRL) for automated on-ramp merging neglects vehicle powertrain and dynamics.
RSCA: Real-time Segmentation-based Context-Aware Scene Text Detection
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.
