Analyzing international political behavior based on similar precedent circumstances is one of the basic techniques that policymakers use to monitor and assess current situations. Our goal is to investigate how to analyze geopolitical conflicts as sequences of events and to determine what probabilistic models are suitable to perform these analyses. In this paper, we evaluate the performance of N-grams models on the problem of forecasting political conflicts from sequences of events. For the current phase of the project, we focused on event data collected from the Balkans war in the 1990's. Our experimental results indicate that N-gram models have impressive results when applied to this data set, with accuracies above 90\% for most configurations.

The dynamic between speaker intent and listener perception is played out in the variation of acoustical cues by the speaker that must be interpreted by the listener to determine in an appropriate way. Emotion speech research must rely on either acted intent (i.e., an actor attempting to express an emotion) or listener perception (i.e., listening tests to assign emotional categories to non-acted data) to define ground truth labels for analysis. The emotion labels are described either using emotion dimension or emotion category. This study examines the two emotion characterization strategies dimension and category in communication of emotion embedded in speech as expressed through acted intent and the perception of emotion determined by a group of listeners. The results reveal that, without context information, intended emotion categories could be perceived by listeners with the averaged accuracy rate five times of chance in category. Also, the trend of listener ratings between emotion dimensions (valence/arousal) and emotional word categories was shown to be well correlated. Furthermore, while listeners confused the specific identity of certain emotional expressions, they were generally very accurate at identifying the intended affective space of the actor as determined by intended valence and arousal.

Algorithms for finding frequent and/or interesting subgraphs in a single large graph scenario are computationally intensive because of the graph isomorphism and the subgraph isomorphism problem. These problems are compounded by the size of most real-world datasets which have sizes in the order of 105 or 106. The SUBDUE algorithm developed by Cook and Holder finds the most compressing subgraph in a large graph. In order to perform the same task on real-world data sets efficiently, Cook et al. developed a parallel approach to SUBDUE called the SP-SUBDUE based on the MPI framework. This paper extends the work done by Cook et al. to improve the efficiency of MPI SUBDUE by modifying the evaluation phase. Our experiments show an improvement in speed-up while retaining the quality of the results of serial SUBDUE. The techniques that we have used in this study can also be used in similar algorithms which use static partitioning of the data and re-evaluation of locally interesting patterns over all the nodes of the cluster.

In reinforcement learning (RL), rewards have been considered the most important feedback in understanding the environment. However, recently there have been interesting forays into other modes such as using sporadic supervisory inputs. This brings into the learning process richer information about the world of interest. In this paper, we model these supervisory inputs as specific types of instructions that provide information in the form of an expert's control decision and certain structural regularities in the state space. We further provide a mathematical formulation for the same and propose a framework to incorporate them into the learning process.

We consider a cross-cultural interaction scenario where a group of soldiers assisted by robots interact with local vendors in a market place. We develop a model to quantify, analyze and predict the perception of the actions of the soldiers and the robot by the local population. The model assumes that humans are considering collections of concrete and intangible values which are not, in general, directly and linearly convertible into each other. We argue that satisfactory modeling accuracy can be achieved by restricting the considered intangibles to a small set of {\em culture sanctioned social values}. For these values, the culture provides a name, calculation methods, as well as associated rules of conduct. We validate our model by comparing the predicted values with the judgment of a large group of human observers cognizant of the modeled culture. We use the model to evaluate the tradeoffs between several long term strategies to maintain security as well as to increase the trust and goodwill of the local population.

We present a framework to represent and reason about nar- ratives. We build a symbolic probabilistic representation of the temporal sequence of world states and events implied by a narrative using statistical approaches. We show that the combination of this representation together with domain knowledge and symbolic probabilistic reasoning algorithms enables understanding of a narrative and answering semantic questions whose responses are not contained in the narrative. In our experiments, we show the power of our framework (vs. traditional approaches) in answering semantic questions for two domains of RoboCup soccer commentaries and early reader children stories focused on spatial contexts.

When analysing social media conversations, in search of the public opinion about an unfolding event that is be- ing discussed in real-time (e.g., presidential debates, major speeches, etc.), it is important to distinguish between two groups of participants: opinion-makers and opinion-holders. To address this problem, we propose a supervised machine-learning approach, which uses inexpensively acquired labeled data from monothematic Twitter accounts to learn a binary classifier for the labels “political account” (opinion-makers) and “non-political account” (opinion-holders). While the classifier has a 83% accuracy on individual tweets, when applied to the last 200 tweets from accounts of a set of 1000 Twitter users, it classifies accounts with a 97% accuracy. This high accuracy derives from our decision to incorporate information about classifier probability into the classification. Our work demonstrates that machine learning algorithms can play a critical role in improving the quality of social media analytics and understanding, whose importance is increasing as social media adoption becomes widespread.

Chairs and committees of the FLAIRS-25 conference.

Interpreting neural spike trains, compressing video, identifying features in DNA microarrays, and recognizing particles in high energy physics all rely upon the ability to find and model complex structure in a high dimensional space. Despite their great promise, high dimensional probabilistic models are frequently computationally intractable to work with in practice. In this thesis I develop solutions to overcome this intractability, primarily in the context of energy based models. A common cause of intractability is that model distributions cannot be analytically normalized. Probabilities can only be computed up to a constant, making training exceedingly difficult. To solve this problem I propose'minimum probability flow learning', a variational technique for parameter estimation in such models.

For successful experimental implementation of any quantum protocol, the quantum states and operations involved must be confirmed to be sufficiently closed to their theoretical targets. One way to obtain such a confirmation is to perform another experiment and from the obtained data make an estimate of the quantum operator involved. Statistically, this is a constrained multiparameter estimation problem - the quantum estimation problem - where we assume we are given a finite number of identical copies of a quantum state or operation, we perform measurements whose mathematical description is assumed to be known, and from the outcome statistics we make our estimate. Due to the probabilistic behavior of the measurement outcomes and the finiteness of the number of measurement trials, there always exist statistical errors in any quantum estimate. The size of the error depends on the choice of measurements and the estimation procedure. In statistics, the former is called an experimental design, while the latter is called an estimator. It is, therefore, a key aim of both classical and quantum estimation theory to find a combination of experimental design and estimator which gives us more precise estimation results using fewer measurement trials. A standard combination in quantum information experiments is that of quantum tomography and maximum likelihood estimator. Although the term "quantum tomography" can be used in several different contexts, we use it to mean an experimental design in which an independently and identically prepared set of measurements are used throughout the entire experiment [1].