Search Results for author:
Found 24 papers, 5 papers with code
AI Coders Are Among Us: Rethinking Programming Language Grammar Towards Efficient Code Generation
Particularly, abundant grammar tokens and formatting tokens are included to make the code more readable to humans.
An Iterative Associative Memory Model for Empathetic Response Generation
Empathetic response generation is to comprehend the cognitive and emotional states in dialogue utterances and generate proper responses.
Exploiting Emotion-Semantic Correlations for Empathetic Response Generation
Based on dynamic emotion-semantic vectors and dependency trees, we propose a dynamic correlation graph convolutional network to guide the model in learning context meanings in dialogue and generating empathetic responses.
Assessing AI Detectors in Identifying AI-Generated Code: Implications for Education
This is achieved by generating code in response to a given question using different variants.
Large Language Models for Software Engineering: A Systematic Literature Review
Nevertheless, a comprehensive understanding of the application, effects, and possible limitations of LLMs on SE is still in its early stages.
BrickPal: Augmented Reality-based Assembly Instructions for Brick Models
The assembly instruction is a mandatory component of Lego-like brick sets. The conventional production of assembly instructions requires a considerable amount of manual fine-tuning, which is intractable for casual users and customized brick sets. Moreover, the traditional paper-based instructions lack expressiveness and interactivity. To tackle the two problems above, we present BrickPal, an augmented reality-based system, which visualizes assembly instructions in an augmented reality head-mounted display.
Source Code Data Augmentation for Deep Learning: A Survey
This paper fills this gap by conducting a comprehensive and integrative survey of data augmentation for source code, wherein we systematically compile and encapsulate existing literature to provide a comprehensive overview of the field.
ASDF: A Differential Testing Framework for Automatic Speech Recognition Systems
To address these limitations, our tool incorporates two novel features: (1) a text transformation module to boost the number of generated test cases and uncover more errors in ASR systems and (2) a phonetic analysis module to identify on which phonemes the ASR system tend to produce errors.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Vector Quantization With Self-Attention for Quality-Independent Representation Learning
During training, we enforce the quantization of features from clean and corrupted images in the same discrete embedding space so that an invariant quality-independent feature representation can be learned to improve the recognition robustness of low-quality images.
BAFFLE: Hiding Backdoors in Offline Reinforcement Learning Datasets
Our experiments conducted on four tasks and four offline RL algorithms expose a disquieting fact: none of the existing offline RL algorithms is immune to such a backdoor attack.
Robust control problems of BSDEs coupled with value functions
A robust control problem is considered in this paper, where the controlled stochastic differential equations (SDEs) include ambiguity parameters and their coefficients satisfy non-Lipschitz continuous and non-linear growth conditions, the objective function is expressed as a backward stochastic differential equation (BSDE) with the generator depending on the value function.
Detecting Offensive Language on Social Networks: An End-to-end Detection Method based on Graph Attention Networks
In this paper, we propose an end-to-end method based on community structure and text features for offensive language detection (CT-OLD).
The $f$-Divergence Reinforcement Learning Framework
In FRL, the policy evaluation and policy improvement phases are simultaneously performed by minimizing the $f$-divergence between the learning policy and sampling policy, which is distinct from conventional DRL algorithms that aim to maximize the expected cumulative rewards.
Fusing Motion Patterns and Key Visual Information for Semantic Event Recognition in Basketball Videos
Based on the observations, we propose a scheme to fuse global and local motion patterns (MPs) and key visual information (KVI) for semantic event recognition in basketball videos.
Addict Free -- A Smart and Connected Relapse Intervention Mobile App
It is widely acknowledged that addiction relapse is highly associated with spatial-temporal factors such as some specific places or time periods.
Discovering Opioid Use Patterns from Social Media for Relapse Prevention
The United States is currently experiencing an unprecedented opioid crisis, and opioid overdose has become a leading cause of injury and death.
Data Centers Job Scheduling with Deep Reinforcement Learning
Efficient job scheduling on data centers under heterogeneous complexity is crucial but challenging since it involves the allocation of multi-dimensional resources over time and space.
Ontology Based Global and Collective Motion Patterns for Event Classification in Basketball Videos
Therefore, a semantic event in broadcast basketball videos is closely related to both the global motion (camera motion) and the collective motion.
Spatial-temporal Multi-Task Learning for Within-field Cotton Yield Prediction
Understanding and accurately predicting within-field spatial variability of crop yield play a key role in site-specific management of crop inputs such as irrigation water and fertilizer for optimized crop production.
Predicting Opioid Relapse Using Social Media Data
In this paper, we introduce a Generative Adversarial Networks (GAN) model to predict the addiction relapses based on sentiment images and social influences.
Forecasting People's Needs in Hurricane Events from Social Network
Our proposed sequence to sequence method forecast people's needs more successfully than either of the other models.
Coordinating Disaster Emergency Response with Heuristic Reinforcement Learning
To improve the efficiency of the emergency response in the immediate aftermath of a disaster, we propose a heuristic multi-agent reinforcement learning scheduling algorithm, named as ResQ, which can effectively schedule the rapid deployment of volunteers to rescue victims in dynamic settings.
Multi-agent Reinforcement Learning
reinforcement-learning
+2
Analysis of the optimal exercise boundary of American put options with delivery lags
A make-your-mind-up option is an American derivative with delivery lags.
Map Matching based on Conditional Random Fields and Route Preference Mining for Uncertain Trajectories
In order to improve offline map matching accuracy of low-sampling-rate GPS, a map matching algorithm based on conditional random fields (CRF) and route preference mining is proposed.
