Recommending Location Privacy Preferences in Ubiquitous Computing

Location-Based Services have become increasingly popular due to the prevalence of smart devices. The protection of users’ location privacy in such systems is a vital issue. Conventional privacy protection methods such as manually predefining privacy rules or asking users to make decisions every time they enter a new location may not be usable, and so researchers have explored the use of machine learning to predict preferences. Model-based machine learning classifiers which are used for prediction may be too computationally complex to be used in real-world applications. We propose a location-privacy recommender that can provide users with recommendations of appropriate location privacy settings through user-user collaborative filtering. We test our scheme on real world dataset and the experiment results show that the performance of our scheme is close to the best performance of model-based classifiers and it outperforms model-based classifiers when there are no sufficient training data.Peer reviewe

Multi-Graph Convolutional Network for Relationship-Driven Stock Movement Prediction

Stock price movement prediction is commonly accepted as a very challenging task due to the volatile nature of financial markets. Previous works typically predict the stock price mainly based on its own information, neglecting the cross effect among involved stocks. However, it is well known that an individual stock price is correlated with prices of other stocks in complex ways. To take the cross effect into consideration, we propose a deep learning framework, called Multi-GCGRU, which comprises graph convolutional network (GCN) and gated recurrent units (GRU) to predict stock movement. Specifically, we first encode multiple relationships among stocks into graphs based on financial domain knowledge and utilize GCN to extract the cross effect based on these pre-defined graphs. To further get rid of prior knowledge, we explore an adaptive relationship learned by data automatically. The cross-correlation features produced by GCN is concatenated with historical records and fed into GRU to model the temporal dependency of stock prices. Experiments on two stock indexes in China market show that our model outperforms other baselines. Note that our model is rather feasible to incorporate more effective stock relationships containing expert knowledge as well as learn relationship on the basis of data dynamically.Comment: 8pages, 4figure

Empirical Study of Deep Learning for Text Classification in Legal Document Review

Predictive coding has been widely used in legal matters to find relevant or privileged documents in large sets of electronically stored information. It saves the time and cost significantly. Logistic Regression (LR) and Support Vector Machines (SVM) are two popular machine learning algorithms used in predictive coding. Recently, deep learning received a lot of attentions in many industries. This paper reports our preliminary studies in using deep learning in legal document review. Specifically, we conducted experiments to compare deep learning results with results obtained using a SVM algorithm on the four datasets of real legal matters. Our results showed that CNN performed better with larger volume of training dataset and should be a fit method in the text classification in legal industry.Comment: 2018 IEEE International Conference on Big Data (Big Data