Users machine learning, becomes ever more complicated, there is can also input their glossaries to seed knowledge graph construction a need to build interactive systems with powerful capabilities for certain attributes. For example, one could input that can be accessed and used by nontechnical domain a glossary of stock ticker symbols to seed the extractions for experts. Such capabilities are especially useful on crawled an attribute'Stock Tickers'. Web data, since many interesting phenomena worthy of social In the second domain exploration phase, domain experts or investigative interest (like fraud), have a significant use the search engine for gaining further insight into domain Web presence. We propose a demonstration of myDIG, a properties and characteristics, and in the case of investigative system that ingests a corpus of webpages stored in a distributed domains, both generating and investigating leads.

We demonstrate Water Advisor, a multi-modal assistant to help non-experts make sense of complex water quality data and apply it to their specific needs. A user can chat with the tool about water quality and activities of interest, and the system tries to advise using available water data for a location, applicable water regulations and relevant parameters using AI methods. Figure 1: Sample advisories - by EPA for Flint residents (left) and by state for visitors (right; Washington State).

Past work in security games has mainly focused on the problem static resource allocation; how to optimally deploy a given fixed team of resources. My research aims to address the challenge of integrating operational planning into security games, where resources are heterogeneous and the defender is tasked with optimizing over both the investment into these resources, as well as their deployment in the field. This allows the defender to design more adaptive strategies, reason about the efficiency of their use of these resources as well as their effectiveness in their deployment. This thesis explores the challenges in integrating these two optimization problems in both the single stage and multi-stage setting and provides a formal model of this problem, which we refer to as the Simultaneous Optimization of Resource Teams and Tactics (SORT) as a new fundamental research problem in security games that combines strategic and tactical decision making. The main contributions of this work are solution methods to the SORT problem under various settings as well as exploring various types of tradeoffs that can arise in these settings. These include managing budget for investment in resources as well as capacity constraints on use of resources. My work addresses scenarios when the tactical decision problem (optimal deployment) is difficult, and thus evaluating the performance of any given team is difficult. Additionally, I address domains where we are tasked with making repeated strategic level decision and where, due to changing domain features, fluctuations in time dependent processes or the realization of uncertain parameters in the problem, it becomes necessary to re-evaluate and adapt to new information.

NASA has acquired more than 22 million images from the planet Mars. To help users find images of interest, we developed a content-based search capability for Mars rover surface images and Mars orbital images. We started with the AlexNet convolutional neural network, which was trained on Earth images, and used transfer learning to adapt the network for use with Mars images. We report on our deployment of these classifiers within the PDS Imaging Atlas, a publicly accessible web interface, to enable the first content-based image search for NASA’s Mars images.

Software vulnerabilities can expose computer systems to attacks by malicious actors. With the number of vulnerabilities discovered in the recent years surging, creating timely patches for every vulnerability is not always feasible. At the same time, not every vulnerability will be exploited by attackers; hence, prioritizing vulnerabilities by assessing the likelihood they will be exploited has become an important research problem. Recent works used machine learning techniques to predict exploited vulnerabilities by analyzing discussions about vulnerabilities on social media. These methods relied on traditional text processing techniques, which represent statistical features of words, but fail to capture their context. To address this challenge, we propose DarkEmbed, a neural language modeling approach that learns low dimensional distributed representations, i.e., embeddings, of darkweb/deepweb discussions to predict whether vulnerabilities will be exploited. By capturing linguistic regularities of human language, such as syntactic, semantic similarity and logic analogy, the learned embeddings are better able to classify discussions about exploited vulnerabilities than traditional text analysis methods. Evaluations demonstrate the efficacy of learned embeddings on both structured text (such as security blog posts) and unstructured text (darkweb/deepweb posts). DarkEmbed outperforms state-of-the-art approaches on the exploit prediction task with an F1-score of 0.74.

Due to the limited natural water resources and the increase in population, managing water consumption is becoming an increasingly important subject worldwide. In this paper, we present and compare different machine learning models that are able to predict water demand for Central Indiana. The models are developed for two different time scales: daily and monthly. The input features for the proposed model include weather conditions (temperature, rainfall, snow), social features (holiday, median income), date (day of the year, month), and operational features (number of customers, previous water demand levels). The importance of these input features as accurate predictors is investigated. The results show that daily and monthly models based on recurrent neural networks produced the best results with an average error in prediction of 1.69% and 2.29%, respectively for 2016. These models achieve a high accuracy with a limited set of input features.

Death is an inevitable part of life and while it cannot be delayed indefinitely it is possible to predict with some certainty when the health of a person is going to deteriorate. In this paper, we predict risk of mortality for patients from two large hospital systems in the Pacific Northwest. Using medical claims and electronic medical records (EMR) data we greatly improve prediction for risk of mortality and explore machine learning models with explanations for end of life predictions. The insights that are derived from the predictions can then be used to improve the quality of patient care towards the end of life.

Over the years, government service provision in China has been plagued by inefficiencies. Previous attempts to address this challenge following a toolbox e-government system model in China were not effective. In this paper, we report on a successful experience in improving government service provision in the domain of social insurance in Shandong Province, China. Through standardization of service workflows following the Complete Contract Theory (CCT) and the infusion of an artificial intelligence (AI) engine to maximize the expected quality of service while reducing waiting time, the Smart Human-resource Services (SmartHS) platform transcends organizational boundaries and improves system efficiency. Deployments in 3 cities involving 2,000 participating civil servants and close to 3 million social insurance service cases over a 1 year period demonstrated that SmartHS significantly improves user experience with roughly a third of the original front desk staff. This new AI-enhanced mode of operation is useful for informing current policy discussions in many domains of government service provision.

Consider a policymaker who wants to decide which intervention to perform in order to change a currently undesirable situation. The policymaker has at her disposal a team of experts, each with their own understanding of the causal dependencies between different factors contributing to the outcome. The policymaker has varying degrees of confidence in the experts’ opinions. She wants to combine their opinions in order to decide on the most effective intervention. We formally define the notion of an effective intervention, and then consider how experts’ causal judgments can be combined in order to determine the most effective intervention. We define a notion of two causal models being compatible , and show how compatible causal models can be combined. We then use it as the basis for combining experts causal judgments. We illustrate our approach on a number of real-life examples.

In this paper, we use the STN framework to study important classes of load scheduling problems that involve metric Efficient algorithms for temporal reasoning are critical for temporal constraints as well as costs of resources. Problems a large number of real-world applications, including autonomous that can be studied in this framework include those that arise space exploration (Knight et al. 2001), domestic in the smart home (Qayyum et al. 2015) and smart grid domains activity management, and job scheduling on servers (Ji, He, (Sianaki, Hussain, and Tabesh 2010) as well as in high and Cheng 2007). Many formalisms have been proposed performance computing (HPC) (Yang et al. 2013) and job and are currently used for reasoning with metric time and shop scheduling (Xiong, Sadeh, and Sycara 1992). Although resources (Smith and Cheng 1993; Kumar 2003; Muscettola the STN framework can be extended to reason about the resource 2004). Simple Temporal Networks (STNs) (Dechter, Meiri, requirements of events (Kumar 2003), in this paper, and Pearl 1991) are popularly used for efficiently reasoning for simplicity of exposition, we reason about the resource about difference constraints in scheduling problems.