We have designed an open and modular course for data science and big data analytics using a workflow paradigm that allows students to easily experience big data through a sophisticated yet easy to use instrument that is an intelligent workflow system. A key aspect of this work is the use of semantic workflows to capture and reuse end-to-end analytic methods that experts would use to analyze big data, and the use of an intelligent workflow system to elaborate the workflow and manage its execution and resulting datasets. Through the exposure of big data analytics in a workflow framework, students will be able to get first-hand experiences with a breadth of big data topics, including multi-step data analytic and statistical methods, software reuse and composition, parallel distributed programming, high-end computing. In addition, students learn about a range of topics in AI, including semantic representations and ontologies, machine learning, natural language processing, and image analysis.

We report on a novel use of parallel coordinates as a pedagogical tool for illustrating the non-intuitive properties of high dimensional spaces with special emphasis on the phenomenon of Curse of Dimensionality. Also, we have collated what we believe to be a representative sample of diverse approaches that exist in literature to conceptualize the Curse of Dimensionality. We envisage that the paper will have pedagogical value in structuring the way Curse of Dimensionality is presented in classrooms and associated lab sessions.

We investigated Parameterized Poker Squares to approximate an optimal game playing agent. We organized our inquiry along three dimensions: partial hand representation, search algorithms, and partial hand utility learning. For each dimension we implemented and evaluated several designs, among which we selected the best strategies to use for BeeMo, our final product. BeeMo uses a parallel flat Monte-Carlo search. The search is guided by a heuristic based on hand patterns utilities, which are learned through an iterative improvement method involving Monte-Carlo simulations and optimized greedy search.

We present a domain-independent approach to reference resolution that allows a robotic or virtual agent to resolve references to entities (e.g., objects and locations) found in open worlds when the information needed to resolve such references is distributed among multiple heterogeneous knowledge bases in its architecture. An agent using this approach can combine information from multiple sources without the computational bottleneck associated with centralized knowledge bases. The proposed approach also facilitates “lazy constraint evaluation”, i.e., verifying properties of the referent through different modalities only when the information is needed. After specifying the interfaces by which a reference resolution algorithm can request information from distributed knowledge bases, we present an algorithm for performing open-world reference resolution within that framework, analyze the algorithm’s performance, and demonstrate its behavior on a simulated robot.

Alzheimer's disease (AD) is a chronic neurodegenerative disease, which involves the degeneration of various brain functions, resulting in memory loss, cognitive disorder and death. Large amounts of multivariate heterogeneous medical test data are available for the analysis of brain deterioration. How to measure the deterioration remains a challenging problem. In this study, we first investigate how different regions of the human brain change as the patient develops AD. Correlation analysis and feature ranking are performed based on the feature vectors from different stages of the pathologic process in Alzheimer disease. Then, an automatic diagnosis system is presented, which is based on a hybrid manifold learning for feature embedding and the bootstrap aggregating (Bagging) algorithm for classification.We investigate two different tasks, i.e. diagnosis and progression prediction. Extensive comparison is made against Support Vector Machines (SVM), Random Forest (RF), Decision Tree (DT) and Random Subspace (RS) methods. Experimental results show that our proposed algorithm yields superior results when compared to the other methods, suggesting promising robustness for possible clinical applications.

Participatory sensing is a promising new low-cost approach for collecting environmental data. However, current large-scale environmental participatory sensing campaigns typically do not coordinate the measurements of participants, which can lead to gaps or redundancy in the collected data. While some work has considered this problem, it has made several unrealistic assumptions. In particular, it assumes that complete and accurate knowledge about the participants future movements is available and it does not consider constraints on the number of measurements a user is willing to take. To address these shortcomings, we develop a computationally-efficient coordination algorithm (Best-match) to suggest to users where and when to take measurements. Our algorithm exploits human mobility patterns, but explicitly considers the inherent uncertainty of these patterns. We empirically evaluate our algorithm on a real-world human mobility and air quality dataset and show that it outperforms the state-of-the-art greedy and pull-based proximity algorithms in dynamic environments.

The lack of reliable data in developing countries is a major obstacle to sustainable development, food security, and disaster relief. Poverty data, for example, is typically scarce, sparse in coverage, and labor-intensive to obtain. Remote sensing data such as high-resolution satellite imagery, on the other hand, is becoming increasingly available and inexpensive. Unfortunately, such data is highly unstructured and currently no techniques exist to automatically extract useful insights to inform policy decisions and help direct humanitarian efforts. We propose a novel machine learning approach to extract large-scale socioeconomic indicators from high-resolution satellite imagery. The main challenge is that training data is very scarce, making it difficult to apply modern techniques such as Convolutional Neural Networks (CNN). We therefore propose a transfer learning approach where nighttime light intensities are used as a data-rich proxy. We train a fully convolutional CNN model to predict nighttime lights from daytime imagery, simultaneously learning features that are useful for poverty prediction. The model learns filters identifying different terrains and man-made structures, including roads, buildings, and farmlands, without any supervision beyond nighttime lights. We demonstrate that these learned features are highly informative for poverty mapping, even approaching the predictive performance of survey data collected in the field.

Conservation is an ethic of sustainable use of natural resources which focuses on the preservation of biodiversity, i.e., the degree of variation of life. Conservation planning seeks to reach this goal by means of deliberate actions, aimed at the protection (or restoration) of biodiversity features. In this paper we present an intelligent system to assist conservation managers in planning habitat restoration actions, with focus on the activities to be carried out in the islands of the Great Barrier Reef (QLD) and the Pilbara (WA) regions of Australia. In particular, we propose a constrained optimisation formulation of the habitat restoration planning (HRP) problem, capturing aspects such as population dynamics and uncertainty. We show that the HRP is NP-hard, and develop a constraint programming (CP) model and a large neighbourhood search (LNS) procedure to generate activity plans under budgeting constraints.

Understanding and predicting how influenza propagates is vital to reduce its impact. In this paper we develop a nonparametric model based on Gaussian process (GP) regression to capture the complex spatial and temporal dependencies present in the data. A stochastic variational inference approach was adopted to address scalability. Rather than modeling the problem as a time-series as in many studies, we capture the space-time dependencies by combining different kernels. A kernel averaging technique which converts spatially-diffused point processes to an area process is proposed to model geographical distribution. Additionally, to accurately model the variable behavior of the time-series, the GP kernel is further modified to account for non-stationarity and seasonality. Experimental results on two datasets of state-wide US weekly flu-counts consisting of 19,698 and 89,474 data points, ranging over several years, illustrate the robustness of the model as a tool for further epidemiological investigations.

This paper considers prescriptive evacuation planning for a region threatened by a natural disaster such a flood, a wildfire, or a hurricane. It proposes a Benders decomposition that generalizes the two-stage approach proposed in earlier work for convergent evacuation plans. Experimental results show that Benders decomposition provides significant improvements in solution quality in reasonable time: It finds provably optimal solutions to scenarios considered in prior work, closing these instances, and increases the number of evacuees by 10 to 15% on average on more complex flood scenarios.