Understanding extreme events, such as hurricanes or forest fires, is of paramount importance because of their adverse impacts on human beings. Such events often propagate in space and time. Predicting-even a few days in advance-what locations will get affected by the event tracks could benefit our society in many ways. Arguably, simulations from “first principles,” where underlying physics-based models are described by a system of equations, provide least reliable predictions for variables characterizing the dynamics of these extreme events. Data-driven model building has been recently emerging as a complementary approach that could learn the relationships between historically observed or simulated multiple, spatio-temporal ancillary variables and the dynamic behavior of extreme events of interest. While promising, the methodology for predictive learning from such complex data is still in its infancy. In this paper, we propose a dynamic networks-based methodology for in-advance prediction of the dynamic tracks of emerging extreme events. By associating a network model of the system with the known tracks, our method is capable of learning the recurrent network motifs that could be used as discriminatory signatures for the event's behavioral class. When applied to classifying the behavior of the hurricane tracks at their early formation stages in Western Africa region, our method is able to predict whether hurricane tracks will hit the land of the North Atlantic region at least 10-15 days lead lag time in advance with more than 90% accuracy using 10-fold cross-validation. To the best of our knowledge, no comparable methodology exists for solving this problem using data-driven models.

We present a novel approach for domain adaptation based on feature grouping and re-weighting. Our algorithm operates by creating an ensemble of multiple classifiers, where each classifier is trained on one particular feature group. Faced with the distribution change involved in domain change, different feature groups exhibit different cross-domain prediction abilities. Herein, ensemble models provide us the flexibility of tuning the weights of corresponding classifiers in order to adapt to the new domain. Our approach is supported by a solid theoretical analysis based on the expressiveness of ensemble classifiers, which allows trading-off errors across source and target domains. Moreover, experimental results on sentiment classification and spam detection show that our approach not only outperforms the baseline method, but is also superior to other state-of-the-art methods.

Today, mobile robots are increasingly expected to operate in ever more complex and dynamic environments.In order to carry out many of the higher level tasks envisioned a semantic understanding of a workspace is pivotal. Here our field has benefited significantly from successes in machine learning and vision: applications in robotics of off-the-shelf object detectors are plentiful. This paper outlines an online, any-time planning framework enabling the active exploration of such detections. Our approach exploits the ability to move to different vantage points and implicitly weighs the benefits of gaining more certainty about the existence of an object against the physical cost of the exploration required. The result is a robot which plans trajectories specifically to decrease the entropy of putative detections. Our system is demonstrated to significantly improve detection performance and trajectory length in simulated and real robot experiments.

Continuous time series data often comprise or contain repeated motifs — patterns that have similar shape, and yet exhibit nontrivial variability. Identifying these motifs, even in the presence of variation, is an important subtask in both unsupervised knowledge discovery and constructing useful features for discriminative tasks. This paper addresses this task using a probabilistic framework that models generation of data as switching between a random walk state and states that generate motifs. A motif is generated from a continuous shape template that can undergo non-linear transformations such as temporal warping and additive noise. We propose an unsupervised algorithm that simultaneously discovers both the set of canonical shape templates and a template-specific model of variability manifested in the data. Experimental results on three real-world data sets demonstrate that our model is able to recover templates in data where repeated instances show large variability. The recovered templates provide higher classification accuracy and coverage when compared to those from alternatives such as random projection based methods and simpler generative models that do not model variability. Moreover, in analyzing physiological signals from infants in the ICU, we discover both known signatures as well as novel physiomarkers.

We propose an online classification approach for co-occurrence data which is based on a simple information theoretic principle. We further show how to properly estimate the uncertainty associated with each prediction of our scheme and demonstrate how to exploit these uncertainty estimates. First, in order to abstain highly uncertain predictions. And second, within an active learning framework, in order to preserve classification accuracy while substantially reducing training set size. Our method is highly efficient in terms of run-time and memory footprint requirements. Experimental results in the domain of text classification demonstrate that the classification accuracy of our method is superior or comparable to other state-of-the-art online classification algorithms.

The original Yahoo! search engine consists of manually organized topic hierarchy of webpages for easy browsing. Modern search engines (such as Google and Bing), on the other hand, return a flat list of webpages based on keywords. It would be ideal if hierarchical browsing and keyword search can be seamlessly combined. The main difficulty in doing so is to automatically (i.e., not manually) classify and rank a massive number of webpages into various hierarchies (such as topics, media types, regions of the world). In this paper we report our attempt towards building this integrated search engine, called SEE (Search Engine with hiErarchy). We implement a hierarchical classification system based on Support Vector Machines, and embed it in SEE. We also design a novel user interface that allows users to dynamically adjust their desire for a higher accuracy vs. more results in any (sub)category of the hierarchy. Though our current search engine is still small (indexing about 1.2 million webpages), the results, including a small user study, have shown a great promise for integrating such techniques in the next-generation search engine.

In times of mass emergency, vast amounts of data are generated via computer-mediated communication (CMC) that are difficult to manually cull and organize into a coherent picture. Yet valuable information is broadcast, and can provide useful insight into time- and safety-critical situations if captured and analyzed properly and rapidly. We describe an approach for automatically identifying messages communicated via Twitter that contribute to situational awareness, and explain why it is beneficial for those seeking information during mass emergencies. We collected Twitter messages from four different crisis events of varying nature and magnitude and built a classifier to automatically detect messages that may contribute to situational awareness, utilizing a combination of hand-annotated and automatically-extracted linguistic features. Our system was able to achieve over 80% accuracy on categorizing tweets that contribute to situational awareness. Additionally, we show that a classifier developed for a specific emergency event performs well on similar events. The results are promising, and have the potential to aid the general public in culling and analyzing information communicated during times of mass emergency.

Manufacturers of TV sets have recently started adding social media features to their products. Some of these products display microblogging messages relevant to the TV show which the user is currently watching. However, such systems suffer from low precision and recall when they use the title of the show to search for relevant messages. Titles of some popular shows such as Lost or Survivor are highly ambiguous, resulting in messages unrelated to the show. Thus, there is a need to develop filtering algorithms that can achieve both high precision and recall. Filtering microblogging messages for Social TV poses several challenges, including lack of training data, lack of proper grammar and capitalization, lack of context due to text sparsity, etc. We describe a bootstrapping algorithm which uses a small manually labeled dataset, a large dataset of unlabeled messages, and some domain knowledge to derive a high precision classifier that can successfully filter microblogging messages which discuss television shows. The classifier is designed to generalize to TV shows which were not part of the training set. The algorithm achieves high precision on our two test datasets and successfully generalizes to unseen television shows. Furthermore, it compares favorably to a text classifier specifically trained on the television shows used for testing.

Social news aggregator services generate readers’ subjective reactions to news opinion articles. Can we use those as a resource to classify articles as liberal or conservative, even without knowing the self-identified political leaning of most users? We applied three semi-supervised learning methods that propagate classifications of political news articles and users as conservative or liberal, based on the assumption that liberal users will vote for liberal articles more often, and similarly for conservative users and articles. Starting from a few labeled articles and users, the algorithms propagate political leaning labels to the entire graph. In cross-validation, the best algorithm achieved 99.6% accuracy on held-out users and 96.3% accuracy on held-out articles. Adding social data such as users’ friendship or text features such as cosine similarity did not improve accuracy. The propagation algorithms, using the subjective liking data from users, also performed better than an SVM based text classifier, which achieved 92.0% accuracy on articles.

The rise in popularity of social networking sites such as Twitter and Facebook has been paralleled by the rise of unwanted, disruptive entities on these networks- — including spammers, malware disseminators, and other content polluters. Inspired by sociologists working to ensure the success of commons and criminologists focused on deterring vandalism and preventing crime, we present the first long-term study of social honeypots for tempting, profiling, and filtering content polluters in social media. Concretely, we report on our experiences via a seven-month deployment of 60 honeypots on Twitter that resulted in the harvesting of 36,000 candidate content polluters. As part of our study, we (1) examine the harvested Twitter users, including an analysis of link payloads, user behavior over time, and followers/following network dynamics and (2) evaluate a wide range of features to investigate the effectiveness of automatic content polluter identification.