This article addresses the modeling of reverberant recording environments in the context of under-determined convolutive blind source separation. We model the contribution of each source to all mixture channels in the time-frequency domain as a zero-mean Gaussian random variable whose covariance encodes the spatial characteristics of the source. We then consider four specific covariance models, including a full-rank unconstrained model. We derive a family of iterative expectationmaximization (EM) algorithms to estimate the parameters of each model and propose suitable procedures to initialize the parameters and to align the order of the estimated sources across all frequency bins based on their estimated directions of arrival (DOA). Experimental results over reverberant synthetic mixtures and live recordings of speech data show the effectiveness of the proposed approach.

Many successful applications of computer vision to image or video manipulation are interactive by nature. However, parameters of such systems are often trained neglecting the user. Traditionally, interactive systems have been treated in the same manner as their fully automatic counterparts. Their performance is evaluated by computing the accuracy of their solutions under some fixed set of user interactions. This paper proposes a new evaluation and learning method which brings the user in the loop. It is based on the use of an active robot user - a simulated model of a human user. We show how this approach can be used to evaluate and learn parameters of state-of-the-art interactive segmentation systems. We also show how simulated user models can be integrated into the popular max-margin method for parameter learning and propose an algorithm to solve the resulting optimisation problem.

Understanding predator-prey relationships among insects is a challenging task in the domain of insect-colony research. This is due to several factors involved, such as determining whether a particular behavior is the result of a predator-prey interaction, a friend-foe interaction or another kind of interaction. In this paper, we analyze a series of predator-prey and friend-foe interactions in two colonies of carpenter ants to better understand and predict such behavior. Using the data gathered, we have also come up with a preliminary model for predicting such behavior under the specific conditions the experiment was conducted in. In this paper, we present the results of our data analysis as well as an overview of the processes involved.

The art of solving the Mastermind puzzle was initiated by Donald Knuth and is already more than 30 years old; despite that, it still receives much attention in operational research and computer games journals, not to mention the nature-inspired stochastic algorithm literature. In this paper we try to suggest a strategy that will allow nature-inspired algorithms to obtain results as good as those based on exhaustive search strategies; in order to do that, we first review, compare and improve current approaches to solving the puzzle; then we test one of these strategies with an estimation of distribution algorithm. Finally, we try to find a strategy that falls short of being exhaustive, and is then amenable for inclusion in nature inspired algorithms (such as evolutionary or particle swarm algorithms). This paper proves that by the incorporation of local entropy into the fitness function of the evolutionary algorithm it becomes a better player than a random one, and gives a rule of thumb on how to incorporate the best heuristic strategies to evolutionary algorithms without incurring in an excessive computational cost.

In this paper we introduce SCARE — the Spatial Cultural Abductive Reasoning Engine, which solves spatial abduction problems (Shakarian, Subrahmanian, and Sapino 2009). We review results of SCARE for activities by Iranian-sponsored “Special Groups” (Kagan, Kagan, and Pletka 2008) operating throughout the Baghdad urban area and compare these findings with new experiments where we predict IED cache sites of the Special Groups in Sadr City. We find that by localizing the spatial abduction problem to a smaller area we obtain greater accuracy - predicting cache sites within 0.33 km as opposed to 0.72 km for all of Baghdad. We suspect that local factors of physical and cultural geography impact reasoning with spatial abduction for this problem.

We present an approach to identifying concurrent patterns of behavior in in-sample temporal factor training data that precede Events of Interest (EoIs). We also present how to use discovered patterns to forecast EoIs in out-of-sample test data. The forecasting methodology is based on matching entities' observed behaviors to patterns discovered in retrospective data. This pattern concept is a generalization of previous pattern definitions. The new pattern concept, based around patterns observed in trends of factor data is based on a finite-state model where observed, sustained trends in a factor map to pattern states. Discovered patterns can be used as a diagnostic tool to better understand the dynamic conditions leading up to specific Event of Interest occurrences and hint at underlying causal structures leading to onsets and terminations of socio-political violence. We present a computationally efficient data-mining method to discover trend patterns. We give an example of using our pattern forecasting methodology to correctly forecast the advent and cessation of ethnic-religious violence in nation states with a low false-alarm rate.

We construct a computer model that allows us to simulate the effect of counterinsurgency operations on a population of agents. We build a society of agents who are interconnected in an established social network. Each agent in this network engages in political discourse with other agents over the legitimacy of the existing government. Agents may decide to support an insurgency, join an insurgency, side with the existing government, or remain neutral over which group to support. Using this model we explore the relative importance of social network structure, influence effectiveness, and combat operation effectiveness in minimizing insurgent strength.

In this paper, we present AutoMed, an automated mediator for multi-issue bilateral negotiation under time constraints. AutoMed uses a qualitative model to represent the negotiators' preferences. It analyzes the negotiators' preferences, monitors the negotiations and proposes possible solutions for resolving the conflict. We conducted experiments in a simulated environment. The results show that negotiations mediated by AutoMed are concluded significantly faster than non-mediated ones and without any of the negotiators opting out. Furthermore, the subjects in the mediated negotiations are more satisfied from the resolutions than the subjects in the non-mediated negotiations.

This paper presents an agent based framework for modeling of the formation and dynamics of pirate networks. The framework consists of (1) development of network formation mechanism and (2) formulation of pirate attack dynamics. Accordingly, the paper attempts to define the characteristics of Pirate Networks and to formulate the rules that govern the operation and evolution of Pirate Networks. We discuss the clan based social system that facilitate pirate formation as well as the pirate network inter-action with the hosting clan system. Using published material, empirical data and surveys the paper attempts to establish credible formation mechanism and operational characterization of pirate attacks. The proposed framework accounts for clan dynamics and the interplay of social, ecological and physical spaces. Finally we conclude with a discussion on exploratory modeling for the refinement of the proposed framework and for empirically grounding proposed simulations.

After building a classifier with modern tools of machine learning we typically have a black box at hand that is able to predict well for unseen data. Thus, we get an answer to the question what is the most likely label of a given unseen data point. However, most methods will provide no answer why the model predicted the particular label for a single instance and what features were most influential for that particular instance. The only method that is currently able to provide such explanations are decision trees. This paper proposes a procedure which (based on a set of assumptions) allows to explain the decisions of any classification method.