Identifying similar narrative sections across longer documents would help identify key events within a corpus, enrich understanding of those events, provide a mechanism for organizing corpora according to their event content, and allow for bottom-up testing of theories of narrative. This paper proposes an automated method for narrative alignment across large textual corpora using techniques from natural language processing and similarity-based image segmentation. This method proceeds by segmenting each document into a series of events, constructs sequences of abstracted representations of those events, compares pairs of sequences to generate image matrices, segments the images, identifies similar segments to discover commonly occurring narrative units, and, finally, returns the source sentences to make the clusters of narrative similarity readable. Preliminary tests of elements of this method were conducted on a small heterogeneous corpus (<; 100 documents) and a moderate heterogeneous corpus (10k documents). Further implementation as described in this position paper is necessary to scale to the full 251k document corpus from which the moderate corpus was drawn.

The popularity of sensor networks and their many uses in critical domains such as military and healthcare make them more vulnerable to malicious attacks. In such contexts, trustworthiness of sensor data and their provenance is critical for decision-making. In this demonstration, we present an efficient and secure approach for transmitting provenance information about sensor data. Our provenance approach uses light-weight in-packet Bloom filters that are encoded as sensor data travels through intermediate sensor nodes, and are decoded and verified at the base station. Our provenance technique is also able to defend against malicious attacks such as packet dropping and allows one to detect the responsible node for packet drops. As such it makes possible to modify the transmission route to avoid nodes that could be compromised or malfunctioning. Our technique is designed to create a trustworthy environment for sensor nodes where only trusted data is processed.