Boosting learning and inference in Markov logic through metaheuristics

Markov Logic (ML) combines Markov networks (MNs) and first-order logic by attaching weights to first-order formulas and using these as templates for features of MNs. State-of-the-art structure learning algorithms in ML maximize the likelihood of a database by performing a greedy search in the space of structures. This can lead to suboptimal results because of the incapability of these approaches to escape local optima. Moreover, due to the combinatorially explosive space of potential candidates these methods are computationally prohibitive. We propose a novel algorithm for structure learning in ML, based on the Iterated Local Search (ILS) metaheuristic that explores the space of structures through a biased sampling of the set of local optima. We show through real-world experiments that the algorithm improves accuracy and learning time over the state-of-the-art algorithms. On the other side MAP and conditional inference for ML are hard computational tasks. This paper presents two algorithms for these tasks based on the Iterated Robust Tabu Search (IRoTS) metaheuristic. The first algorithm performs MAP inference and we show through extensive experiments that it improves over the state-of-the-art algorithm in terms of solution quality and inference time. The second algorithm combines IRoTS steps with simulated annealing steps for conditional inference and we show through experiments that it is faster than the current state-of-the-art algorithm maintaining the same inference quality.     

A hybrid positioning system for technology‐independent location‐aware computing

Location‐aware computing is a form of context‐aware mobile computing that refers to the ability of providing users with services that depend on their position. Locating the user terminal, often called positioning, is essential in this form of computing. Towards this aim, several technologies exist, ranging from personal area networking, to indoor, outdoor, and up to geographic area systems. Developers of location‐aware software applications have to face with a number of design choices, that typically depend on the chosen technology. This work addresses the problem of easing the development of pull location‐aware applications, by allowing uniform access to multiple heterogeneous positioning systems. Towards this aim, the paper proposes an approach to structure location‐aware mobile computing systems in a way independent of positioning technologies. The approach consists in structuring the system into a layered architecture, that provides application developers with a standard Java Application Programming Interface (JSR‐179 API), and encapsulates location data management and technology‐specific positioning subsystems into lower layers with clear interfaces. In order to demonstrate the proposed approach we present the development of HyLocSys. It is an open hybrid software architecture designed to support indoor/outdoor applications, which allows the uniform (combined or separate) use of several positioning technologies. HyLocSys uses a hybrid data model, which allows the integration of different location information representations (using symbolic and geometric coordinates). Moreover, it allows support to handset‐ and infrastructure‐based positioning approaches while respecting the privacy of the user. The paper presents a prototypal implementation of HyLocSys for heterogeneous scenarios. It has been implemented and tested on several platforms and mobile devices. Copyright © 2009 John Wiley & Sons, Ltd.     

Evaluating maps produced by urban search and rescue robots: lessons learned from RoboCup

This paper presents the map evaluation methodology developed for the Virtual Robots Rescue competition held as part of RoboCup. The procedure aims to evaluate the quality of maps produced by multi-robot systems with respect to a number of factors, including usability, exploration, annotation and other aspects relevant to robots and first responders. In addition to the design choices, we illustrate practical examples of maps and scores coming from the latest RoboCup contest, outlining strengths and weaknesses of our modus operandi. We also show how a benchmarking methodology developed for a simulation testbed effortlessly and faithfully transfers to maps built by a real robot. A number of conclusions may be derived from the experience reported in this paper and a thorough discussion is offered.     

Apparent mass matrix of standing subjects exposed to multi-axial whole-body vibration

This paper describes the experimental characterisation of the apparent mass matrix of eight male subjects in standing position and the identification of nonlinearities under both mono-axial and dual-axis whole-body vibration. The nonlinear behaviour of the response was studied using the conditioned response techniques considering models of increasing complexity. Results showed that the cross-axis terms are comparable to the diagonal terms. The contribution of the nonlinear effects are minor and can be endorsed to the change of modal parameters during the tests. The nonlinearity generated by the vibration magnitude is more evident in the subject response, since magnitude-dependent effects in the population are overlaid by the scatter in the subjects’ biometric data. The biodynamic response is influenced by the addition of a secondary vibration axis and, in case of dual-axis vibrations, the overall magnitude has a marginal contribution.

Practitioner Summary: We have measured both the diagonal and cross-axis elements of the apparent mass matrix. The effect of nonlinearities and the simultaneous presence of vibration along two axes are smaller than the inter-subject variability.     

Pairwise Registration by Local Orientation Cues

Inspired by recent work on robust and fast computation of 3D Local Reference Frames (LRFs), we propose a novel pipeline for coarse registration of 3D point clouds. Key to the method are: (i) the observation that any two corresponding points endowed with an LRF provide a hypothesis on the rigid motion between two views, (ii) the intuition that feature points can be matched based solely on cues directly derived from the computation of the LRF, (iii) a feature detection approach relying on a saliency criterion which captures the ability to establish an LRF repeatably. Unlike related work in literature, we also propose a comprehensive experimental evaluation based on diverse kinds of data (such as those acquired by laser scanners, Kinect and stereo cameras) as well as on quantitative comparison with respect to other methods. We also address the issue of setting the many parameters that characterize coarse registration pipelines fairly and realistically. The experimental evaluation vouches that our method can handle effectively data acquired by different sensors and is remarkably fast.     

Organizational Learning Mechanisms and Creative Climate: Insights from an Italian Fashion Design Company

This paper investigates the relationship between different types of organizational learning mechanisms and creative climate. In the context of an action research study, this paper focuses on insights from a survey that was administered to all the employees of the Product Design and Development unit of the company. The results demonstrate that the three different types of organizational learning mechanisms considered in the study (cognitive, structural and procedural mechanisms) are associated with creative climate. The study generates new scientific knowledge about the role of organizational learning mechanisms and provides specific recommendations for organizations that aim to enhance creative climate.