Body expressions influence recognition of emotions in the face and voice.

The most familiar emotional signals consist of faces, voices, and whole-body expressions, but so far research on emotions expressed by the whole body is sparse. The authors investigated recognition of whole-body expressions of emotion in three experiments. In the first experiment, participants performed a body expression-matching task. Results indicate good recognition of all emotions, with fear being the hardest to recognize. In the second experiment, two alternative forced choice categorizations of the facial expression of a compound face-body stimulus were strongly influenced by the bodily expression. This effect was a function of the ambiguity of the facial expression. In the third experiment, recognition of emotional tone of voice was similarly influenced by task irrelevant emotional body expressions. Taken together, the findings illustrate the importance of emotional whole-body expressions in communication either when viewed on their own or, as is often the case in realistic circumstances, in combination with facial expressions and emotional voices. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Design and implementation of a platform for configuring clinical dynamic safety checklist applications

In recent years, it has been demonstrated that checklists can improve patient safety significantly. To facilitate the effective use of checklists in daily practice, both the medical community and the informatics community propose to implement checklists in dynamic checklist applications that can be integrated into the clinical workflow and that is specific to the patient context. However, it is difficult to develop such applications because they are tightly intertwined with the content of specific checklists. We propose a platform that enables access to dynamic checklist applications by configuring the infrastructures provided in the platform. Then, the applications can be developed without time-consuming programming work. We define a number of design criteria regarding point of care and clinical processes by analyzing the existing checklist applications and the lessons learned from implementations. Then, by applying rule-based clinical decision support and workflow management technologies, we design technical mechanisms to satisfy the design criteria. A dynamic checklist application platform is designed based on these mechanisms. Finally, we build a platform in various design cycle iterations, driven by multiple clinical cases. By applying the platform, we develop nine comprehensive dynamic checklist applications with 242 dynamic checklists. The results demonstrate both the feasibility and the overall generic nature of the proposed approach. We propose a novel platform for configuring dynamic checklist applications. This platform satisfies the general requirements and can be easily configured to satisfy different scenarios in which safety checklists are used.     

Mapping and monitoring riparian vegetation distribution,structure and composition with regression tree models and post-classification change metrics

Riparian systems have become increasingly susceptible to both natural and human disturbances as cumulative pressures from changing land use and climate alter the hydrological regimes. This article introduces a landscape dynamics monitoring protocol that incorporates riparian structural classes into the land-cover classification scheme and examines riparian change within the context of surrounding land-cover change. We tested whether Landsat Thematic Mapper (TM) imagery could be used to document a riparian tree die-off through the classification of multi-date Landsat images using classification and regression tree (CART) models trained with physiognomic vegetation data. We developed a post-classification change map and used patch metrics to examine the magnitude and trajectories of riparian class change relative to mapped disturbance parameters. Results show that catchments where riparian change occurred can be identified from land-cover change maps; however, the main change resulting from the die-off disturbance was compositional rather than structural, making accurate post-classification change detection difficult.     

Iterative spectral unmixing (ISU)

Spectral unmixing techniques strive to find proportions of end-members within a pixel from the observed mixed pixel spectrum and a number of pure end-member spectra of known composition. The outcomes of such analysis are fraction (abundance) images for the selected (pure) end-members and a root mean square (RMS) error estimate representing the difference between the observed mixed spectrum and the calculated mixed spectrum. The RMS image can be used to select additional end-members and re-position existing ones. This is now done manually. In this Letter, an automatediterative approach is proposed using the RMS error image to select additional end-members and re-distribute older ones in order toincrease the accuracy of the spectral unmixing. Optimization criteria are proposed to drive the iterative process including minimization of the average RMS, minimizing the spread of the RMS values, minimizing the spatial structure of the RMS image, minimizing the spatial anisotropy of the RMS image and minimizing the local variance. The preliminary results of the analysis indicate that considerable improvement tothe spectral unmixing results are achieved using the iterative spectral unmixing (ISU) approach.     

A method for minimising of low frequency and unwrapping artefacts from interferometric calculations

On 7 September 1999 a magnitude M_W = 5.9 earthquake occurred in the Athens area of Greece producing a subsidence of 6–7 cm detected by radar interferometry. This study introduces a processing technique, which produces a clear deformation pattern of the earthquake, mostly released from artefacts due to orbital effects, unwrapping cycle slipping errors and atmospherics disturbances. A set of 17 ERS‐1 and ERS‐2 SAR images acquired between December 1997 and January 2001 has been used. The contribution of each artefact to the interferograms was calculated, both in the frequency and spatial domains.     

Robust stabilization of nonlinear stochastic 2‐D systems: LaSalle‐type theorem approach

LaSalle theorem (also known as the LaSalle invariance principle) plays an essential role in the systems and control theory. Recently, it has been extensively studied and developed for various types of one‐dimensional (1‐D) systems including deterministic and stochastic 1‐D systems in discrete‐ and continuous‐time domains. For two‐dimensional (2‐D) systems, such studies have received considerably less attention. In this article, based on discrete martingale theory, a LaSalle‐type theorem is first developed for a class of discrete‐time nonlinear stochastic 2‐D systems described by a Roesser model. The proposed result can be regarded as an extension of stochastic Lyapunov‐like theorem, which guarantees the convergence almost surely of system state trajectories. Extensions to the problem of optimal guaranteed cost control of nonlinear stochastic 2‐D systems are also presented. The proposed schemes are then utilized to derive tractable synthesis conditions of a suboptimal state‐feedback controller for uncertain 2‐D systems with multiplicative stochastic noises. The effectiveness of the obtained results is illustrated by given numerical examples and simulations.     

Is Relevance Relevant? Market,Science, and War: Discourses of Search Engine Quality

In the face of rising controversy about search engine results—that they are too restrictive, too comprehensive, lacking in certain areas, over-represented in others—this article presents the results of in-depth interviews with search engine producers, examining their conceptions of search engine quality and the implications of those conceptions. Structuration theory suggests that the cultural schemas that frame these discourses of quality will be central in mobilizing resources for technological development. The evidence presented here suggests that resources in search engine development are overwhelmingly allocated on the basis of market factors or scientific/technological concerns. Fairness and representativeness, core elements of the journalists' definition of quality media content, are not key determiners of search engine quality in the minds of search engine producers. Rather, alternative standards of quality, such as customer satisfaction and relevance, mean that tactics to silence or promote certain websites or site owners (such as blacklisting, whitelisting, and index "cleaning") are seen as unproblematic.     

The pairwise attribute noise detection algorithm

Analyzing the quality of data prior to constructing data mining models is emerging as an important issue. Algorithms for identifying noise in a given data set can provide a good measure of data quality. Considerable attention has been devoted to detecting class noise or labeling errors. In contrast, limited research work has been devoted to detecting instances with attribute noise, in part due to the difficulty of the problem. We present a novel approach for detecting instances with attribute noise and demonstrate its usefulness with case studies using two different real-world software measurement data sets. Our approach, called Pairwise Attribute Noise Detection Algorithm (PANDA), is compared with a nearest neighbor, distance-based outlier detection technique (denoted DM) investigated in related literature. Since what constitutes noise is domain specific, our case studies uses a software engineering expert to inspect the instances identified by the two approaches to determine whether they actually contain noise. It is shown that PANDA provides better noise detection performance than the DM algorithm. Jason Van Hulse is a Ph.D. candidate in the Department of Computer Science and Engineering at Florida Atlantic University. His research interests include data mining and knowledge discovery, machine learning, computational intelligence and statistics. He is a student member of the IEEE and IEEE Computer Society. He received the M.A. degree in mathematics from Stony Brook University in 2000, and is currently Director, Decision Science at First Data Corporation. Taghi M. Khoshgoftaar is a professor at the Department of Computer Science and Engineering, Florida Atlantic University, and the director of the Empirical Software Engineering and Data Mining and Machine Learning Laboratories. His research interests are in software engineering, software metrics, software reliability and quality engineering, computational intelligence, computer performance evaluation, data mining, machine learning, and statistical modeling. He has published more than 300 refereed papers in these subjects. He has been a principal investigator and project leader in a number of projects with industry, government, and other research-sponsoring agencies. He is a member of the IEEE, the IEEE Computer Society, and IEEE Reliability Society. He served as the program chair and general chair of the IEEE International Conference on Tools with Artificial Intelligence in 2004 and 2005, respectively. Also, he has served on technical program committees of various international conferences, symposia, and workshops. He has served as North American editor of the Software Quality Journal, and is on the editorial boards of the journals Empirical Software Engineering, Software Quality, and Fuzzy Systems. Haiying Huang received the M.S. degree in computer engineeringfrom Florida Atlantic University, Boca Raton, Florida, USA, in 2002. She is currently a Ph.D. candidate in the Department of Computer Science and Engineering at Florida Atlantic University. Her research interests include software engineering, computational intelligence, data mining, software measurement, software reliability, and quality engineering.     

Discontinuous Galerkin Approximations for Computing Electromagnetic Bloch Modes in Photonic Crystals

We analyze discontinuous Galerkin finite element discretizations of the Maxwell equations with periodic coefficients. These equations are used to model the behavior of light in photonic crystals, which are materials containing a spatially periodic variation of the refractive index commensurate with the wavelength of light. Depending on the geometry, material properties and lattice structure these materials exhibit a photonic band gap in which light of certain frequencies is completely prohibited inside the photonic crystal. By Bloch/Floquet theory, this problem is equivalent to a modified Maxwell eigenvalue problem with periodic boundary conditions, which is discretized with a mixed discontinuous Galerkin (DG) formulation using modified Nédélec basis functions. We also investigate an alternative primal DG interior penalty formulation and compare this method with the mixed DG formulation. To guarantee the non-pollution of the numerical spectrum, we prove a discrete compactness property for the corresponding DG space. The convergence rate of the numerical eigenvalues is twice the minimum of the order of the polynomial basis functions and the regularity of the solution of the Maxwell equations. We present both 2D and 3D numerical examples to verify the convergence rate of the mixed DG method and demonstrate its application to computing the band structure of photonic crystals.