Crowdsourcing is a popular paradigm for effectively collecting labels at low cost. The Dawid-Skene estimator has been widely used for inferring the true labels from the noisy labels provided by non-expert crowdsourcing workers. However, since the estimator maximizes a non-convex log-likelihood function, it is hard to theoretically justify its performance. In this paper, we propose a two-stage efficient algorithm for multi-class crowd labeling problems. The first stage uses the spectral method to obtain an initial estimate of parameters. Then the second stage refines the estimation by optimizing the objective function of the Dawid-Skene estimator via the EM algorithm. We show that our algorithm achieves the optimal convergence rate up to a logarithmic factor. We conduct extensive experiments on synthetic and real datasets. Experimental results demonstrate that the proposed algorithm is comparable to the most accurate empirical approach, while outperforming several other recently proposed methods.

The problem of transferring skills to hyper-redundant system requires the design of new motion primitive representations that can cope with multiple sources of noise and redundancy, and that can dynamically handle perturbations in the environment. One way is to take inspiration from invertebrate systems in nature to seek for new versatile representations of motion/behavior primitives for continuum robots. In particular, the incredibly varied skills achieved by the octopus can guide us toward the design of such robust encoding scheme. This abstract presents our ongoing work that aims at combining statistical machine learning, dynamical systems and stochastic optimization to study the problem of transferring skills to a flexible surgical robot (STIFF-FLOP) composed of 2 modules with constant curvatures. The approach is tested in simulation by imitation and self-refinement of an octopus reaching motion.

An appealing alternative to tediously specifying robot behaviors in response to particular image features is to have the robot’s behavior be induced by human decisions made when piloting the robot. This paper presents one promising approach to creating this alternative. A human pilots a camera-equipped robot, which builds a representation of its target environment using Growing Neural Gas (GNG). The robot associates an action with each GNG node based on what the human pilot was doing while the node was active. When running autonomously, the robot chooses the action associated with the node that is the closest match to the current input image. Preliminary results suggest that the approach has potential, but that subsequent alteration of the actions induced for some of the GNG nodes is important for acceptable performance.

In this paper we present a novel agent-based modeling methodology to predict rooftop solar adoptions in the residential energy market. We first applied several linear regression models to estimate missing variables for non-adopters, so that attributes of non-adopters and adopters could be used to train a logistic regression model. Then, we integrated the logistic regression model along with other predictive models into a multi-agent simulation platform and validated our models by comparing the forecast of aggregate adoptions in a typical zip code area with its ground truth. This result shows that the agent-based model can reliably predict future adoptions. Finally, based on the validated agent-based model, we compared the outcome of a hypothesized seeding policy with the original incentive plan, and investigated other alternative seeding policies which could lead to more adopters.

Using geospatial methods, this paper informs the evolving field of research on the diffusion of residential Third Party Owned PV systems by analyzing 1) the spatial distribution of TPO systems, and 2) the influence of demographics on the adoption on the local level. This research is part of a multidisciplinary study into the diffusion of solar technology (SEEDS), using San Diego County as focus area. Our findings reveal a significant clustering of TPO PV adoption in San Diego County. TPO systems reached a similarly high market share across a large area in the central county in contrast to the installation of host-owned systems, which have been less evenly distributed across single-family households in the same area. The diffusion of TPO systems in San Diego County can be partially explained by looking at median income and percentage of people born in the US. The explanatory power of the model varies across the region.

Touch can be a powerful means of communication especially when it is combined with other sensing modalities, such as speech. The challenge on a humanoid robot is to sense touch in a way that can be sensitive to subtle cues, such as the hand used and amount of force applied. We propose a novel combination of sensing modalities to extract touch information. We extract hand information using the Leap Motion active sensor, then determine force information from force sensitive resistors. We combine these sensing modalities at the feature level, then train a support vector machine to recognize specific touch gestures. We demonstrate a high level of accuracy recognizing four different touch gestures from the firefighting domain.

In signal analysis and synthesis, linear approximation theory considers a linear decomposition of any given signal in a set of atoms, collected into a so-called dictionary. Relevant sparse representations are obtained by relaxing the orthogonality condition of the atoms, yielding overcomplete dictionaries with an extended number of atoms. More generally than the linear decomposition, overcomplete kernel dictionaries provide an elegant nonlinear extension by defining the atoms through a mapping kernel function (e.g., the gaussian kernel). Models based on such kernel dictionaries are used in neural networks, gaussian processes and online learning with kernels. The quality of an overcomplete dictionary is evaluated with a diversity measure the distance, the approximation, the coherence and the Babel measures. In this paper, we develop a framework to examine overcomplete kernel dictionaries with the entropy from information theory. Indeed, a higher value of the entropy is associated to a further uniform spread of the atoms over the space. For each of the aforementioned diversity measures, we derive lower bounds on the entropy. Several definitions of the entropy are examined, with an extensive analysis in both the input space and the mapped feature space.

A product concept evaluation system combining conjoint analysis with prediction markets is developed. It is also proposed how to determine the payoff for each prediction security corresponding to a product concept, so as to have participants to behave truthfully in the market. Further, how the proposed system works is investigated by evolutionary game simulation.

Quality control is one of the central issues in crowdsourcing research. In this paper, we consider a quality control problem of crowdsourced enumeration tasks that request workers to enumerate possible answers as many as possible. Since workers neither necessarily provide correct answers nor provide exactly the same answers even if the answers indicate the same idea, we propose a two-stage quality control method consisting of the answer clustering stage and the reliability estimation stage.

One of the most popular uses of crowdsourcing is to provide training data for supervised machine learning algorithms. Since human annotators often make errors, requesters commonly ask multiple workers to label each example.  But is this strategy always the most cost effective use of crowdsourced workers? We argue "No" --- often classifiers can achieve higher accuracies when trained with noisy "unilabeled" data. However, in some cases relabeling is extremely important.  We discuss three factors that may make relabeling an effective strategy: classifier expressiveness, worker accuracy, and budget.