Intelligent Systems and Technology (TIST)


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ACM Transactions on Intelligent Systems and Technology (TIST) - Regular Papers, Volume 9 Issue 2, January 2018

Section: Regular Papers

Knowledge Representations and Inference Techniques for Medical Question Answering
Travis R. Goodwin, Sanda M. Harabagiu
Article No.: 14
DOI: 10.1145/3106745

Answering medical questions related to complex medical cases, as required in modern Clinical Decision Support (CDS) systems, imposes (1) access to vast medical knowledge and (2) sophisticated inference techniques. In this article, we examine the...

SocialWave: Visual Analysis of Spatio-temporal Diffusion of Information on Social Media
Guodao Sun, Tan Tang, Tai-Quan Peng, Ronghua Liang, Yingcai Wu
Article No.: 15
DOI: 10.1145/3106775

Rapid advancement of social media tremendously facilitates and accelerates the information diffusion among users around the world. How and to what extent will the information on social media achieve widespread diffusion across the world? How can...

Supervised Representation Learning with Double Encoding-Layer Autoencoder for Transfer Learning
Fuzhen Zhuang, Xiaohu Cheng, Ping Luo, Sinno Jialin Pan, Qing He
Article No.: 16
DOI: 10.1145/3108257

Transfer learning has gained a lot of attention and interest in the past decade. One crucial research issue in transfer learning is how to find a good representation for instances of different domains such that the divergence between domains can...

Understanding and Identifying Rhetorical Questions in Social Media
Suhas Ranganath, Xia Hu, Jiliang Tang, Suhang Wang, Huan Liu
Article No.: 17
DOI: 10.1145/3108364

Social media provides a platform for seeking information from a large user base. Information seeking in social media, however, occurs simultaneously with users expressing their viewpoints by making statements. Rhetorical questions have the form of...

Iteratively Divide-and-Conquer Learning for Nonlinear Classification and Ranking
Ou Wu, Xue Mao, Weiming Hu
Article No.: 18
DOI: 10.1145/3122802

Nonlinear classifiers (i.e., kernel support vector machines (SVMs)) are effective for nonlinear data classification. However, nonlinear classifiers are usually prohibitively expensive when dealing with large nonlinear data. Ensembles of linear...

Stopping Criterion for Active Learning with Model Stability
Yexun Zhang, Wenbin Cai, Wenquan Wang, Ya Zhang
Article No.: 19
DOI: 10.1145/3125645

Active learning selectively labels the most informative instances, aiming to reduce the cost of data annotation. While much effort has been devoted to active sampling functions, relatively limited attention has been paid to when the learning...

SPACE-TA: Cost-Effective Task Allocation Exploiting Intradata and Interdata Correlations in Sparse Crowdsensing
Leye Wang, Daqing Zhang, Dingqi Yang, Animesh Pathak, Chao Chen, Xiao Han, Haoyi Xiong, Yasha Wang
Article No.: 20
DOI: 10.1145/3131671

Data quality and budget are two primary concerns in urban-scale mobile crowdsensing. Traditional research on mobile crowdsensing mainly takes sensing coverage ratio as the data quality metric rather than the overall sensed data error in the...

Vertical Ensemble Co-Training for Text Classification
Gilad Katz, Cornelia Caragea, Asaf Shabtai
Article No.: 21
DOI: 10.1145/3137114

High-quality, labeled data is essential for successfully applying machine learning methods to real-world text classification problems. However, in many cases, the amount of labeled data is very small compared to that of the unlabeled, and labeling...