Mothers' attributions regarding their children's social behavior and personality characteristics.

Sixty mothers of four 12-yr-old children described their children's positive and negative characteristics and specific instances of their children's desirable and undesirable social behavior. They rated (a) the environmental versus dispositional basis of their child's behavior, (b) the behavior's probable origins, cross-situational consistency, and temporal stability, (c) the child's controllability, and (d) personal responsibility for engaging in each behavior. As predicted from attribution theory, mothers perceived desirable child behaviors as dispositional and undesirable ones as unstable and situationally caused. Positive personal characteristics were seen as stable and inborn, but negative ones as transitory. Children perceived as difficult to manage, however, were also seen as dispositionally and stably oppositional. Older children were considered more difficult to manage than younger ones, but only their misbehavior was seen as more innately determined. Girls' conduct of all types was considered more dispositional, and sons' characteristics were seen as more stable, especially during adolescence. The results suggest a positive bias in mothers' perceptions of their children, except when they are considered difficult to control. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

SmallWorlds: Visualizing Social Recommendations

We present SmallWorlds, a visual interactive graph‐based interface that allows users to specify, refine and build item‐preference profiles in a variety of domains. The interface facilitates expressions of taste through simple graph interactions and these preferences are used to compute personalized, fully transparent item recommendations for a target user. Predictions are based on a collaborative analysis of preference data from a user's direct peer group on a social network. We find that in addition to receiving transparent and accurate item recommendations, users also learn a wealth of information about the preferences of their peers through interaction with our visualization. Such information is not easily discoverable in traditional text based interfaces. A detailed analysis of our design choices for visual layout, interaction and prediction techniques is presented. Our evaluations discuss results from a user study in which SmallWorlds was deployed as an interactive recommender system on Facebook.     

Combination of canonical correlation analysis and empirical mode decomposition applied to denoising the labor electrohysterogram

The electrohysterogram (EHG) is often corrupted by electronic and electromagnetic noise as well as movement artifacts, skeletal electromyogram, and ECGs from both mother and fetus. The interfering signals are sporadic and/or have spectra overlapping the spectra of the signals of interest rendering classical filtering ineffective. In the absence of efficient methods for denoising the monopolar EHG signal, bipolar methods are usually used. In this paper, we propose a novel combination of blind source separation using canonical correlation analysis (BSS_CCA) and empirical mode decomposition (EMD) methods to denoise monopolar EHG. We first extract the uterine bursts by using BSS_CCA then the biggest part of any residual noise is removed from the bursts by EMD. Our algorithm, called CCA_EMD, was compared with wavelet filtering and independent component analysis. We also compared CCA_EMD with the corresponding bipolar signals to demonstrate that the new method gives signals that have not been degraded by the new method. The proposed method successfully removed artifacts from the signal without altering the underlying uterine activity as observed by bipolar methods. The CCA_EMD algorithm performed considerably better than the comparison methods.