The paper uses the SCOP theory of concepts to model the role of environmental context on three levels of entrepreneurial opportunity: idea generation, idea development, and entrepreneurial decision. The role of contextual-fit in the generation and development of ideas is modeled as the collapse of their superposition state into one of the potential states that composes this superposition. The projection of this collapsed state on the socio-economic basis results in interference between the developed idea and the perceptions of the supporting community, undergoing an eventual collapse for an entrepreneurial decision that reflects the shared vision of its stakeholders. The developed idea may continue to evolve due to continuous or discontinuous changes in the environment. The model offers unique insights into the effects of external influences on entrepreneurial decisions.

When general purpose software agents fail, it's often because they're brittle and need more background commonsense knowledge. In this paper we present relation properties as a valuable type of commonsense knowledge that can be automatically inferred at scale by reading the Web. People base many commonsense inferences on their knowledge of relation properties such as functionality, transitivity, and others. For example, all people know that bornIn(Year) satisfies the functionality property, meaning that each person can be born in exactly one year. Thus inferences like "Obama was born in 1961, so he was not born in 2008", which computers do not know, are obvious even to children. We demonstrate scalable heuristics for learning relation functionality from noisy Web text that outperform existing approaches to detecting functionality. The heuristics we use address Web NLP challenges that are also common to learning other relation properties, and can be easily transferred. Each relation property we learn for a Web-scale set of relations will enable computers to solve real tasks, and the data from learning many such properties will be a useful addition to general commonsense knowledge bases.

Using computational intelligence, our ultimate goal is to self-regulate systems composed of humans, machines and robots. Self-regulation is important for the control of mixed organizations and systems. An overview of self-regulation for organizations and systems, characterized by our solution of the tradeoffs between Fourier pairs of Gaussian distributions that affect decision-making differently, is provided. A mathematical outline of our solution and a sketch of future plans are provided.

Traditional dialogue management systems are tightly coupled with the sensing ability of a single computer. How to organize an interaction in pervasive environments to provide a friendly and integrated interface to users is an important issue. This requires a transition of the human-computer interaction (HCI) from tight coupling to loose coupling. This paper proposes a hierarchical multimodal framework for pervasive interactions. Our system is designed to remind the activities of daily living for individuals with cognitive impairments.The system is composed of Markov decision processes for activity planing, and multimodal partially observable Markov decision processes for action planning and executing. Empirical results demonstrate the hierarchical multimodal framework establishes a flexible mechanism for pervasive interaction systems.

To assist scientists in data analysis tasks, we have developed semantic workflow representations that support automatic constraint propagation and reasoning algorithms to manage constraints among the individual workflow steps. Semantic constraints can be used to represent requirements of input datasets as well as best practices for the method represented in a workflow. We demonstrate how the Wings workflow system uses semantic workflows to assist users in creating workflows while validating that the workflows comply with the requirements of the software components and datasets. Wings reasons over semantic workflow representations that consist of both a traditional dataflow graph as well as a network of constraints on the data and components of the workflow.

The emergence of low-cost sensing architectures for diverse modalities has made it possible to deploy sensor networks that acquire large amounts of very high-dimensional data. To cope with such a data deluge, manifold models are often developed that provide a powerful theoretical and algorithmic framework for capturing the intrinsic structure of data governed by a low-dimensional set of parameters. However, these models do not typically take into account dependencies among multiple sensors. We thus propose a new joint manifold framework for data ensembles that exploits such dependencies. We show that joint manifold structure can lead to improved performance for manifold learning. Additionally, we leverage recent results concerning random projections of manifolds to formulate a universal, network-scalable dimensionality reduction scheme that efficiently fuses the data from all sensors.

In this paper, we present a method for isometric correction of manifold learning techniques. We first present an isometric nonlinear dimension reduction method. Our proposed method overcomes the issues associated with well-known isometric embedding techniques such as ISOMAP and maximum variance unfolding (MVU), i.e., computational complexity and the geodesic convexity requirement. Based on the proposed algorithm, we derive our isometric correction method. Our approach follows an isometric solution to the problem of local tangent space alignment. We provide a derivation of a fast iterative solution. The performance of our algorithm is illustrated on both synthetic and real datasets compared to other methods.

Researchers in many fields such as machine learning, computer vision, bioinformatics and robotics often observe that high dimensional data samples have low degrees of freedom in local neighborhoods, but a more complicated global structure. In many cases, there is enough structure in the data so the degrees of freedom can be described by a lower dimensional object such as a manifold. The goal of manifold learning research is to discover techniques that exploit local structure in data to learn better models, learn better input-output relationships and reduce the computational complexity of learning. The field of manifold learning is truly cross-disciplinary, involving researchers from such varied fields as topology, geometry, machine learning, statistics, computer vision, robotics and many others. This has led to an accelerating pace of research and applications in recent years.

Researchers can use simulators Figure 1: The Siros client/server architecture of the Konbini to collect data to build and evaluate interaction models at system. the same time as core components of the real-world robot are built and integrated. Once the real robot becomes robust enough, the models trained on simulators can be applied for Clients are in charge of rendering a given view of the virtual further experiments.

In order to design robots suited to engage in cooperative manipulation tasks with humans, we study human-human teams as they work together to move a heavy object across a room. We are interested in several questions. First, do two person, gross motion tasks follow the same sinusoidal pattern, one person fine motion tasks do? Does performance improve when audio or visual communication is permitted? How does performance correlate with an individual's perception of performance? Non-physiological, or performance based, studies of human-human cooperation can be divided into two categories: Haptic and Non-Haptic (audio, visual, etc). The first category, involves physical interaction through the object being manipulated via force, pressure, and tactile sensations (Jones and Sarter 2008), (Reed and Peshkin 2008). Most of the non-haptic experiments are virtual setups where individuals are moving an object together on a computer screen via two controllers (Basdogan, Ho, and Srinivasan 2000), (Sallnas and Zhai 2003). A survey on the role of communication between people appears in (Whitaker, 2003). The novelty of our work is to investigate non-haptic communication in haptic manipulation tasks.