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Co-saliency detection is a newly emerging and rapidly growing research area in the computer vision community. As a novel branch of visual saliency, co-saliency detection refers to the discovery of common and salient foregrounds from two or more...
Virtual Metering: An Efficient Water Disaggregation Algorithm via Nonintrusive Load Monitoring
Bingsheng Wang, Zhiqian Chen, Arnold P. Boedihardjo, Chang-Tien Lu
Article No.: 39
The scarcity of potable water is a critical challenge in many regions around the world. Previous studies have shown that knowledge of device-level water usage can lead to significant conservation. Although there is considerable interest in...
A Multi-Label Multi-View Learning Framework for In-App Service Usage Analysis
Yanjie Fu, Junming Liu, Xiaolin Li, Hui Xiong
Article No.: 40
The service usage analysis, aiming at identifying customers’ messaging behaviors based on encrypted App traffic flows, has become a challenging and emergent task for service providers. Prior literature usually starts from segmenting a...
Question retrieval, which aims to find similar versions of a given question, is playing a pivotal role in various question answering (QA) systems. This task is quite challenging, mainly in regard to five aspects: synonymy, polysemy, word order,...
A Novel Image-Centric Approach Toward Direct Volume Rendering
Naimul Mefraz Khan, Riadh Ksantini, Ling Guan
Article No.: 42
Transfer function (TF) generation is a fundamental problem in direct volume rendering (DVR). A TF maps voxels to color and opacity values to reveal inner structures. Existing TF tools are complex and unintuitive for the users who are more likely...
Quick Bootstrapping of a Personalized Gaze Model from Real-Use Interactions
Michael Xuelin Huang, Jiajia Li, Grace Ngai, Hong Va Leong
Article No.: 43
Understanding human visual attention is essential for understanding human cognition, which in turn benefits human--computer interaction. Recent work has demonstrated a Personalized, Auto-Calibrating Eye-tracking (PACE) system, which makes it...
A Bayesian Approach to Intervention-Based Clustering
Igor Kulev, Pearl Pu, Boi Faltings
Article No.: 44
An important task for intelligent healthcare systems is to predict the effect of a new intervention on individuals. This is especially true for medical treatments. For example, consider patients who do not respond well to a new drug or have...
Sparse Passive-Aggressive Learning for Bounded Online Kernel Methods
Jing Lu, Doyen Sahoo, Peilin Zhao, Steven C. H. Hoi
Article No.: 45
One critical deficiency of traditional online kernel learning methods is their unbounded and growing number of support vectors in the online learning process, making them inefficient and non-scalable for large-scale applications. Recent studies on...
Evolutionary Strategy to Perform Batch-Mode Active Learning on Multi-Label Data
Oscar Reyes, Sebastián Ventura
Article No.: 46
Multi-label learning has become an important area of research owing to the increasing number of real-world problems that contain multi-label data. Data labeling is an expensive process that requires expert handling. The annotation of multi-label...
Modeling Queries with Contextual Snippets for Information Retrieval
Qin Chen, Qinmin Hu, Jimmy Xiangji Huang, Liang He
Article No.: 47
Query expansion under the pseudo-relevance feedback (PRF) framework has been extensively studied in information retrieval. However, most expansion methods are mainly based on the statistics of single terms, which can generate plenty of irrelevant...
Recent decades have witnessed the rapid growth of educational data mining (EDM), which aims at automatically extracting valuable information from large repositories of data generated by or related to people’s learning activities in...