Data Mining - Concepts and Techniques.

We will review some of the major results in random graphs and some of the more challenging open problems. We will cover algorithmic and structural questions. We will touch on newer models, including those related to the WWW.

Random graphs

“Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks”の学習報告

Social Network Analysis

Emotion and Adaptation

Recently, sparse representation attracts widespread attention in the field of pattern recognition and artificial intelligence. Gabor features based sparse representation classifier had been introduced in face recognition research field and achieved good recognition effect. But its enormous time cost and space cost and computational complexity in the real system cannot be ignored. So this paper proposed a novel facial expression recognition method based on monogenic binary pattern and sparse representation. The proposed method extracts features from the facial expression image by using monogenic binary pattern and utilizes information entropy to weight the feature block, then constructs sparse dictionary and makes use of residual value for classification. Our proposed method was tested on the popular FERET and Cohn-Kanade facial database and obtained the average rates 94.92% and 97.46% respectively. The experimental results show that the sparse dictionary which is constructed by the proposed features has stronger ability for sparse representation and has better performance in time and space efficiency.

Facial Expression Recognition Based on MBP and Sparse Representation

In this paper, we study the network Lifetime Maximization problem in Mobile healthcare sensor systems (LMM) For the healthecare system, we consider a dynamic scenario where users are mobile at their own wills and periodically report their personal health information (PHI) to a static sink, eg a powerful server, for further processing and distributing The objective is to optimize the network lifetime by flow scheduling The major difficulty lies in the time-dependent network topologies Therefore, we propose a novel temporal-spatial network modeling method by extending current model with time dimension Based on this model, we show that if the movement of users are known in advance (ie offline case), the problem can be optimally solved in polynomial time by a linear programming However, the online LMM problem is much more difficult to tackle, since we prove that there exists no online algorithm with a constant performance ratio to the offline optimal algorithm in terms of the network lifetime We further design simulations to show the performance gap between online and offline LMM Considering the user mobility within a given scenario follows some certain patterns, we show the potential improvements of using a prediction-based method This investigation provides certain insights on designing efficient online algorithms for the LMM problem

Fuji Ren

Papers

Facial Expression Recognition Based on MBP and Sparse Representation

Network lifetime optimization in wireless healthcare systems: Understanding the gap between online and offline scenarios