When constructing visualization pipelines using toolkits, developers must understand what sequencing of operators will transform their data from its raw state to some requested visual representation. In some cases, the requested visual representation must be generated from hybrid pipelines, composed of both toolkit-based and custom operators. Traditionally, developers learn about how to construct these visualization pipelines by word of mouth, by reading documentation and by inspecting code examples, all of which can be costly in terms of time and effort expended. The Visualization Knowledge Project (VisKo) is built on a knowledge base of visualization toolkit operators including rules for how operators are chained together to form pipelines. VisKo helps scientists by automatically generating and suggesting fully functional visualization pipelines, alleviating scientists from having to write any pipeline code. This paper reports on the kinds of knowledge required to support automatic pipeline generation as well our successes when applying VisKo to a number of visualizations scenarios spanning geophysics, environmental and materials science.

His informatics group built the reusable software platform for Stembook Despite the fact that we now have access to almost all peer reviewed (www.stembook.org), William Cohen exchanged and is orthogonal to any specific biomedical domain The growing size of the scientific literature has led to a number of ontology. We believe this approach will be extremely useful in attempts to automatically extract entities and relationships from drug discovery to break down information silos, increase information scientific papers, and then to populate databases with this extracted awareness and sharing, and integrate terminologies and information. In my group we have been exploring techniques data with documents and text, both public and private. We will for using this sort of extracted information for specific tasks, discuss applications we are currently developing in collaboration including "bootstrapping" to improve the coverage of an extraction with a major pharma.

Images form a significant, yet under-utilized, information source in published biomedical articles. Much current work on biomedical image retrieval and classification uses simple, standard image representation employing features such as edge direction or gray scale histograms. In our earlier work we have used such features as well to classify images, where image-class-tags have been used to represent and classify complete articles. Here we focus on a different literature classification task: identifying articles discussing cis-regulatory elements and modules, motivated by the need to understand complex gene-networks. Curators attempting to identify such articles use as a major cue a certain type of image in which the conserved cis-regulatory region on the DNA is shown. Our experiments show that automatically identifying such images using common image features (such as gray scale) is highly error prone. However, using Optical Character Recognition (OCR) to extract alphabet characters from images, calculating character distribution and using the distribution parameters as image features, forms a novel image representation, which allows us to identify DNA-content in images with high precision and recall (over 0.9). Utilizing the occurrence of DNA-rich images within articles, we train a classifier to identify articles pertaining to cis-regulatory elements with a similarly high precision and recall. Using OCR-based image features has much potential beyond the current task, to identify other types of biomedical sequence-based images showing DNA, RNA and proteins. Moreover, automatically identifying such images is applicable beyond the current use-case, in other important biomedical document classification tasks.

This paper provides a comparison between alternate styles and tecnhiques for controlling many subordinate agents: delegation vs. swarm "control" or influence. Each management style is defined and pros and cons articulated. The author then attempts to apply a model he created in prior work of the "tradeoff space" of automation control approaches along three dimensions: competence, workload and unpredictability. This application offers insights about the strengths and weaknesses of each approach, but also points to a limitation in the characterization of the tradeoff space.

PubMed queries frequently retrieve thousands of documents making it very challenging for a user to identify information of interest. In this paper we propose a method for automatically identifying central concepts in large PubMed retrievals. The centrality of concept is modeled using the hypergeometric distribution. Retrieved documents are grouped by concept, which can help users navigate the retrieval. We test our method on five datasets, each representing a medical condition.

Intelligent wheelchairs can help increase independent mobility for elderly residents with cognitive impairment, who are currently excluded from the use of powered wheelchairs. This paper presents three case studies, demonstrating the efficacy of the NOAH (Navigation and Obstacle Avoidance Help) system. The findings reported can be used to refine our understanding of user needs and help identify methods to improve the quality of life of the intended users.

Creating an AI device that is both easy to control and comfortable to interact with will likely require algorithms for accurately interpreting conversational speech. Homonyms and homophones represent a particular challenge in this regard, thus the study of puns and other forms of humorous wordplay can be informative. Moving beyond the simple resolution of word uncertainty to an understanding of humor is, however, problematic. The Mutual Vulnerability Theory of Laughter identifies numerous variables involved in our differentiating humorous and nonhumorous stimuli. These include available information, type and degree of relationship with others, personal history, culture, and even mood. It also suggests there will be potential liabilities for AI users, retailers, and developers resulting from even successful attempts to identify, respond to, and create humor, as all require the highlighting of vulnerabilities.

Unpaid caregivers for persons with cognitive impairments provide a valuable medical service at a personal cost to themselves (both health and financial). Smart systems in the home can potentially ease the caregiver burden but the home is a difficult environment for smart systems to operate. This work in progress examines the design of a smart caregiver support system and how it is extended in a new system to support working caregivers. The system uses AI in a human-in-the-loop approach.

Statistical models almost always yield predictions that are more accurate than those of human experts. However, humans are better at data acquisition and at recognizing atypical circumstances. We use prediction markets to combine predictions from groups of humans and artificial-intelligence agents and show that they are more robust than those from groups of humans or agents alone.

In this paper we describe the architecture of the OntoGene Relation mining pipeline and some of its recent applications. With this research overview paper we intend to provide a contribution towards the recently started discussion towards standards for information extraction architectures in the biomedical domain. Our approach delivers domain entities mentioned in each input document, as well as candidate relationships, both ranked according to a confidency score computed by the system. This information is presented to the user through an advanced interface aimed at supporting the process of interactive curation.