Author name cluster

Xiaofeng Yu

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

4 papers

1 author row

JBHI Journal 2026 Journal Article

Avatar-Based Picture Exchange Communication System Enhancing Joint Attention Training for Children With Autism

Yongjun Ren
Runze Liu
Huinan Sang
Xiaofeng Yu

Children with Autism Spectrum Disorder (ASD) often struggle with social communication and feel anxious in interactive situations. The Picture Exchange Communication System (PECS) is commonly used to enhance basic communication skills in children with ASD, but it falls short in reducing social anxiety during therapist interactions and in keeping children engaged. This paper proposes the use of virtual character technology alongside PECS training to address these issues. By integrating a virtual avatar, children's communication skills and ability to express needs can be gradually improved. This approach also reduces anxiety and enhances the interactivity and attractiveness of the training. After conducting a T-test, it was found that PECS assisted by a virtual avatar significantly improves children's focus on activities and enhances their behavioral responsiveness. To address the problem of poor accuracy of gaze estimation in unconstrained environments, this study further developed a visual feature-based gaze estimation algorithm, the three-channel gaze network (TCG-Net). It utilizes binocular images to refine the gaze direction and infer the primary focus from facial images. Our focus was on enhancing gaze tracking accuracy in natural environments, crucial for evaluating and improving Joint Attention (JA) in children during interactive processes. TCG-Net achieved an angular error of 4. 0 on the MPIIGaze dataset, 5. 0 on the EyeDiap dataset, and 6. 8 on the RT-Gene dataset, confirming the effectiveness of our approach in improving gaze accuracy and the quality of social interactions.

Details DOI

IJCAI Conference 2015 Conference Paper

A Unified Probabilistic Model of User Activities and Relations on Social Networking Sites

Xiaofeng Yu
Junqing Xie
Shuai Wang

In this work, we investigate the bidirectional mutual interactions (BMI) between users’ activities and user-user relationships on social networking sites. We analyze and study the fundamental mechanism that drives the characteristics and dynamics of BMI is the underlying social influence. We make an attempt at a unified probabilistic approach, called joint activity and relation (JAR), for modeling and predicting users’ activities and user-user relationships simultaneously in a single coherent framework. Instead of incorporating social influence in an ad hoc manner, we show that social influence can be captured quantitatively. Based on JAR, we learn social influence between users and users’ personal preferences for both user activity prediction and user-user relation discovery through statistical inference. To address the challenges of the introduced multiple layers of hidden variables in JAR, we propose a new learning algorithm based on expectation maximization (EM) and we further propose a powerful and efficient generalization of the EM based algorithm for model fitting. We show that JAR exploits mutual interactions and benefits, by taking advantage of the learned social influence and users’ personal preferences, for enhanced user activity prediction and user-user relation discovery. We further experiment with real world dataset to verify the claimed advantages achieving substantial performance gains.

PDF Details

AAAI Conference 2010 Conference Paper

Bidirectional Integration of Pipeline Models

Xiaofeng Yu
Wai Lam

Traditional information extraction systems adopt pipeline strategies, which are highly ineffective and suffer from several problems such as error propagation. Typically, pipeline models fail to produce highly-accurate final output. On the other hand, there has been growing interest in integrated or joint models which explore mutual benefits and perform multiple subtasks simultaneously to avoid problems caused by pipeline models. However, building such systems usually increases computational complexity and requires considerable engineering. This paper presents a general, strongly-coupled, and bidirectional architecture based on discriminatively trained factor graphs for information extraction. First we introduce joint factors connecting variables of relevant subtasks to capture dependencies and interactions between them. We then propose a strong bidirectional MCMC sampling inference algorithm which allows information to flow in both directions to find the approximate MAP solution for all subtasks. Extensive experiments on entity identification and relation extraction using real-world data illustrate the promise of our approach.

PDF Details

AAAI Conference 2008 Conference Paper

Hidden Dynamic Probabilistic Models for Labeling Sequence Data

Xiaofeng Yu

We propose a new discriminative framework, namely Hidden Dynamic Conditional Random Fields (HD- CRFs), for building probabilistic models which can capture both internal and external class dynamics to label sequence data. We introduce a small number of hidden state variables to model the sub-structure of a observation sequence and learn dynamics between different class labels. An HDCRF offers several advantages over previous discriminative models and is attractive both, conceptually and computationally. We performed experiments on three well-established sequence labeling tasks in natural language, including part-of-speech tagging, noun phrase chunking, and named entity recognition. The results demonstrate the validity and competitiveness of our model. In addition, our model compares favorably with current state-of-the-art sequence labeling approach, Conditional Random Fields (CRFs), which can only model the external dynamics.

PDF Details