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Taking the advantage of the well-established defect chemistry of SrTiO3, acceptor and donor doped SrTiO3 single crystals are used as model systems to understand the roles of oxygen vacancies and the Schottky barrier in the resistive switching. More importantly, SrTiO3 based memristive devices are used to emulate the neurological and psychological functions of the brain. The synaptic plasticity is achieved with Ni/Nb-SrTiO3/Ti memristive devices, and the learning and forgetting processes of the brain, together with the resultant explicit and implicit memories, are also realized with the device. Associative learning, a classical learning case of the brain, is demonstrated as well. The emulation of various neurological and psychological functions in a single memristive device simplifies the construction of the artificial neural network and facilitates the advent of the artificial intelligence. In this work, materials science becomes directly related to neurology and psychology.

This paper describes an innovative approach for studying interaction between humans and care robots. Using live theatrical performance, we developed a play that depicts a plausible, future care scenario between a human and a socially assistive robot. We used an expanded version of the Godspeed Questionnaire to measure the audiences’ reactions to the robot, the observed interactions between the human and the robot, and their overall reactions to the performance. We present our results and propose a methodology and guidelines for using applied theatre as a platform to study human robot interaction (HRI). Unlike other HRI studies, the subject of our research is not the user who interacts with the robot but rather the audiences observing the HRI. We consider the technical and artistic challenges of designing and staging a believable care scenario that could potentially influence the perception and acceptance of care robots. This study marks a first step towards designing a robust framework for combining applied theatre with HRI research.

AMORE is a hybrid recommendation system that provides movie recommenda- tion functionality to video-on-demand subscribers of a major triple-play service provider in Greece. Without any user relevance feedback for movies available, all recommendations are solely based on the users’ viewing history. To overcome such limitations as well as the extra problem of user histories that are usually the merger of the preferences of all persons in each household, we have performed extensive experiments with open-source recommendation software such as Apache Mahout and Lens-Kit, as well as with our own implementa- tions of several user-based, item-based, and content-based recommendation algorithms. Our results indicate that our own custom multi-threaded implementation of collaborative filtering combined with a custom content-based algorithm outperforms current state-of-the-art imple- mentations of similar algorithms both in solution quality and in response time by margins exceeding 100 % in terms of recall quality and 6300 % in terms of running time. The hybrid nature of the ensemble allows the system to perform well and to overcome inherent limitations of collaborative filtering, such as various cold-start problems. AMORE has been deployed in a production environment where it has contributed to an increase in the provider’s rental profits, while at the same time offers customer retention support.

Adaptive foreground segmentation is traditionally performed using Stauffer & Grimson’s algorithm that models every pixel of the frame by a mixture of Gaussian distributions with continuously adapted parameters. In this paper we provide an enhancement of the algorithm by adding two important dynamic elements to the baseline algorithm: The learning rate can change across space and time, while the Gaussian distributions can be merged together if they become similar due to their adaptation process. We quantify the importance of our enhancements and the effect of parameter tuning using an annotated outdoors sequence.

In Bayesian analysis of a statistical model, the predictive distribution is obtained by marginalizing over the parameters with their posterior distributions. Compared to the frequently used point estimate plug-in method, the predictive distribution leads to a more reliable result in calculating the predictive likelihood of the new upcoming data, especially when the amount of training data is small. The Bayesian estimation of a Dirichlet mixture model (DMM) is, in general, not analytically tractable. In our previous work, we have proposed a global variational inference-based method for approximately calculating the posterior distributions of the parameters in the DMM analytically. In this paper, we extend our previous study for the DMM and propose an algorithm to calculate the predictive distribution of the DMM with the local variational inference (LVI) method. The true predictive distribution of the DMM is analytically intractable. By considering the concave property of the multivariate inverse beta function, we introduce an upper-bound to the true predictive distribution. As the global minimum of this upper-bound exists, the problem is reduced to seek an approximation to the true predictive distribution. The approximated predictive distribution obtained by minimizing the upper-bound is analytically tractable, facilitating the computation of the predictive likelihood. With synthesized data and real data evaluations, the good performance of the proposed LVI based method is demonstrated by comparing with some conventionally used methods.

The circle geometric constraint model (CGCM) was put forward for resolving the open-pit mine ore-matching problems (OMOMP). By adopting the approaches of graph theory, block model of blasted piles was abstracted into a set of nodes and directed edges, which were connected together with other nodes in the range of circle constraints, to describe the mining sequence. Also, the constructing method of CGCM was introduced in detail. The algorithm of CGCM has been realized in the DIMINE system, and applied to a short-term (5 d) program calculation for ore-matching of a cement limestone mine in Hebei Province, China. The applications show that CGCM can well describe the mining sequence of ore blocks and its mining geometric constraints in the process of mining blasted piles. This model, which is applicable for resolving OMOMP under complicated geometric constraints with accurate results, provides effective ways to solve the problems of open-pit ore-matching.