Node Attribution Method (NAMA)
In order to solve the problem that convolutional neural networks (CNN) are difficult to process non-image type relational data, Kipf et al. proposed a graph convolutional neural network (GCN). The core idea is to perform two-fold information fusion for each node in a given graph during each iteration: the fusion of graph structure information and the fusion of node feature dimensions. Although GCN has been widely used in the fields of scene semantic relationship analysis, natural language processing, and few-shot learning because of its ability to combine generalization, owing to its two-information fusion involves mathematical irreversible calculations, it is hard for GCN to explain that the predicting reason for each node classification (i.e. attribution analysis). However, the existing attribution analysis methods cannot be directly applied to the GCN because compared with the independence among CNN input data, there is correlation between GCN input data. This leads to the existing attribution method only to obtain the partial contribution of the final decision of the GCN from target node feature, the complete contribution and the contribution from neighbor nodes features cannot be obtained. To this end, we propose a gradient attribution analysis method for GCN, NAM (Node Attribution Method), can get the contribution of the target node and its neighbor nodes to the GCN output. We also propose the NIV (Node Importance Visualization) method to visualize the target node of the GCN and its neighbor nodes based on the value of the contribution value. We use the perturbation analysis method to verify the effect of NAM based on the citation network dataset. The experimental results show that NAM can well learn the contribution of each node to the node classification prediction. …

Generative One-Shot Learning (GOL)
Highly Autonomous Driving (HAD) systems rely on deep neural networks for the visual perception of the driving environment. Such networks are trained on large manually annotated databases. In this work, a semi-parametric approach to one-shot learning is proposed, with the aim of bypassing the manual annotation step required for training perceptions systems used in autonomous driving. The proposed generative framework, coined Generative One-Shot Learning (GOL), takes as input single one-shot objects, or generic patterns, and a small set of so-called regularization samples used to drive the generative process. New synthetic data is generated as Pareto optimal solutions from one-shot objects using a set of generalization functions built into a generalization generator. GOL has been evaluated on environment perception challenges encountered in autonomous vision. …

Secondary Data Analysis (SDA)
Many research questions can be answered quickly and efficiently using data already collected for previous research. This practice is called secondary data analysis (SDA), and has gained popularity due to lower costs and improved research efficiency. In this paper we propose DFS, a file system to standardize the metadata representation of datasets, and DDU, a scalable architecture based on DFS for semi-automated metadata generation and data recommendation on the cloud. We discuss how DFS and DDU lays groundwork for automatic dataset aggregation, how it integrates with existing data wrangling and machine learning tools, and explores their implications on datasets stored in digital libraries. …

Block Point Process Model (BPPM)
Many application settings involve the analysis of timestamped relations or events between a set of entities, e.g. messages between users of an on-line social network. Static and discrete-time network models are typically used as analysis tools in these settings; however, they discard a significant amount of information by aggregating events over time to form network snapshots. In this paper, we introduce a block point process model (BPPM) for dynamic networks evolving in continuous time in the form of events at irregular time intervals. The BPPM is inspired by the well-known stochastic block model (SBM) for static networks and is a simpler version of the recently-proposed Hawkes infinite relational model (IRM). We show that networks generated by the BPPM follow an SBM in the limit of a growing number of nodes and leverage this property to develop an efficient inference procedure for the BPPM. We fit the BPPM to several real network data sets, including a Facebook network with over 3, 500 nodes and 130, 000 events, several orders of magnitude larger than the Hawkes IRM and other existing point process network models. …