109 Result(s)
within
Yi**ang Chen
Artificial Intelligence
Data Mining and Knowledge Discovery
Information Storage and Retrieval
-
Chapter and Conference Paper
Causal Discovery with Bayesian Networks Inductive Transfer
Bayesian networks (BNs) is a dominate model for representing causal knowledge with uncertainty. Causal discovery with BNs requiring large amount of training data for learning BNs structure. When confronted wit...
-
Chapter and Conference Paper
Information Diffusion Model Based on Opportunity, Trust and Motivation
Building an accurate information diffusion model around universal social factors has started to post its popularity on social network researches, which benefits a lot from its evolution simulation for identify...
-
Chapter and Conference Paper
A Refined MISD Algorithm Based on Gaussian Process Regression
Time series data is a common data type in real life, and modelling of time series data along with its underlying temporal dynamics is always a challenging job. Temporal point process is an outstanding method t...
-
Chapter and Conference Paper
An Image Processing Method via OpenCL for Identification of Pulmonary Nodules
Lung cancer is one of the most diagnosable form of cancer worldwide. Recent researches have showed that the diagnoses of pulmonary nodules in Computed Tomography (CT) chest scans based on deep learning have ma...
-
Chapter and Conference Paper
Leveraging Local Interactions for Geolocating Social Media Users
Predicting the geolocation of social media users is one of the core tasks in many applications, such as rapid disaster response, targeted advertisement, and recommending local events. In this paper, we introdu...
-
Chapter and Conference Paper
Corrosion Prediction on Sewer Networks with Sparse Monitoring Sites: A Case Study
Sewer corrosion is a widespread and costly issue for water utilities. Knowing the corrosion status of a sewer network could help the water utility to improve efficiency and save costs in sewer pipe maintenance...
-
Chapter and Conference Paper
Unified User and Item Representation Learning for Joint Recommendation in Social Network
Friend and item recommendation in online social networks is a vital task, which benefits for both users and platform providers. However, extreme sparsity of user-user matrix and user-item matrix issue create s...
-
Chapter and Conference Paper
Multi-label Classification via Label-Topic Pairs
The task of learning from multi-label example is rather challenging because of the tremendous number of possible label sets. It has been well recognized that exploiting label relationships in a proper way can ...
-
Chapter and Conference Paper
Identifying Scholarly Communities from Unstructured Texts
Scholarly community detection has important applications in various fields. Previous studies have relied heavily on structured scholar networks, which have high computational complexity and are challenging to ...
-
Chapter and Conference Paper
What Decides the Dropout in MOOCs?
Based on the datasets from the MOOCs of Peking University running on the Coursera platform, we extract 19 major features of tune in after analyzing the log structure. To begin with, we focus on the characteris...
-
Chapter and Conference Paper
Human Action Recognition Based on Sub-data Learning
Human action recognizing nowadays plays a key role in varieties of computer vision applications while at the same time it’s quite challenging for the requirement of accuracy and robustness. Most current comput...
-
Chapter and Conference Paper
Exploiting Geographical Location for Team Formation in Social Coding Sites
Social coding sites (SCSs) such as GitHub and BitBucket are collaborative platforms where developers from different background (e.g., culture, language, location, skills) form a team to contribute to a shared ...
-
Chapter and Conference Paper
Image-Text Dual Model for Small-Sample Image Classification
Small-sample classification is a challenging problem in computer vision and has many applications. In this paper, we propose an image-text dual model to improve the classification performance on small-sample d...
-
Chapter and Conference Paper
Dependency-Attention-Based LSTM for Target-Dependent Sentiment Analysis
Target-dependent sentiment analysis is a fine-grained sentiment analysis and has received an increasing attention. For target-dependent sentiment analysis, the key issue is to capture the important context inf...
-
Chapter and Conference Paper
Discovering Both Explicit and Implicit Similarities for Cross-Domain Recommendation
Recommender System has become one of the most important techniques for businesses today. Improving its performance requires a thorough understanding of latent similarities among users and items. This issue is ...
-
Chapter
DAAR: A Discrimination-Aware Association Rule Classifier for Decision Support
Undesirable correlations between sensitive attributes (such as race, gender or personal status) and the class label (such as recruitment decision and approval of credit card), may lead to biased decision in da...
-
Chapter and Conference Paper
Adaptive One-Class Support Vector Machine for Damage Detection in Structural Health Monitoring
Machine learning algorithms have been employed extensively in the area of structural health monitoring to compare new measurements with baselines to detect any structural change. One-class support vector machi...
-
Chapter and Conference Paper
Exploring Celebrities on Inferring User Geolocation in Twitter
Location information of social media users provides crucial context to monitor real-time events such as natural disasters, terrorism and epidemics. Since only a small amount of social media data are geotagged,...
-
Chapter and Conference Paper
A Study of Players’ Experiences During Brain Games Play
Much of the experience of videogame players remains hidden. This paper presents an empirical study that assesses the experience of 50 participants (i.e. 25 children and 25 adults) during brain games play. Resu...
-
Chapter and Conference Paper
Efficient Computation of Continuous Range Skyline Queries in Road Networks
Skyline query processing in road networks has been investigated extensively in recent years. Skyline points for road network applications may be large while the query point may only interest the ones within a ...
