Effects of imperfect automation on decision making in a simulated command and control task

OBJECTIVE: Effects of four types of automation support and two levels of automation reliability were examined. The objective was to examine the differential impact of information and decision automation and to investigate the costs of automation unreliability. BACKGROUND: Research has shown that imperfect automation can lead to differential effects of stages and levels of automation on human performance. Method: Eighteen participants performed a "sensor to shooter" targeting simulation of command and control. Dependent variables included accuracy and response time of target engagement decisions, secondary task performance, and subjective ratings of mental work-load, trust, and self-confidence. RESULTS: Compared with manual performance, reliable automation significantly reduced decision times. Unreliable automation led to greater cost in decision-making accuracy under the higher automation reliability condition for three different forms of decision automation relative to information automation. At low automation reliability, however, there was a cost in performance for both information and decision automation. CONCLUSION: The results are consistent with a model of human-automation interaction that requires evaluation of the different stages of information processing to which automation support can be applied. APPLICATION: If fully reliable decision automation cannot be guaranteed, designers should provide users with information automation support or other tools that allow for inspection and analysis of raw data.     

A practical approximation algorithm for the LMS line estimator

The problem of fitting a straight line to a finite collection of points in the plane is an important problem in statistical estimation. Robust estimators are widely used because of their lack of sensitivity to outlying data points. The least median-of-squares (LMS) regression line estimator is among the best known robust estimators. Given a set of n points in the plane, it is defined to be the line that minimizes the median squared residual or, more generally, the line that minimizes the residual of any given quantile q, where 0<q?1. This problem is equivalent to finding the strip defined by two parallel lines of minimum vertical separation that encloses at least half of the points.The best known exact algorithm for this problem runs in O(n²) time. We consider two types of approximations, a residual approximation, which approximates the vertical height of the strip to within a given error bound ε_r?0, and a quantile approximation, which approximates the fraction of points that lie within the strip to within a given error bound ε_q?0. We present two randomized approximation algorithms for the LMS line estimator. The first is a conceptually simple quantile approximation algorithm, which given fixed q and ε_q>0 runs in O(nlogn) time. The second is a practical algorithm, which can solve both types of approximation problems or be used as an exact algorithm. We prove that when used as a quantile approximation, this algorithm's expected running time is . We present empirical evidence that the latter algorithm is quite efficient for a wide variety of input distributions, even when used as an exact algorithm.     

Precision of the CAESAR scan-extracted measurements

Three-dimensional (3D) body scanners are increasingly used to derive 1D body dimensions from 3D whole body scans for instance, as input for clothing grading systems to make made-to-measure clothing or for width and depth dimensions of a seated workstation. In this study, the precision of the scanner-derived 1D dimensions from the CAESAR survey, a multinational anthropometric survey, was investigated. Two combinations of scanning teams with 3D whole body scanners were compared, one called the US Team and the other the Dutch Team. Twenty subjects were measured three times by one scanner and one team, and three times by the other combination. The subjects were marked prior to scanning using small dots, and the linear distances between the dots were calculated after processing the scans. The mean absolute difference (MAD) of the repetitions was calculated and this was compared to reported acceptable errors in manual measurements from the US Army's ANSUR survey when similar measurements were available. In addition, the coefficient of variation (CV) was calculated for all measurements. The results indicate that the CAESAR scan-extracted measurements are highly reproducible; for most measures the MAD is less than 5mm. In addition, more than 93% of the MAD values for CAESAR are significantly smaller than the ANSUR survey acceptable errors. Therefore, it is concluded that the type of scan-extracted measures used in CAESAR are as good as or better than comparable manual measurements. Scan-extracted measurements that do not use markers or are not straight-line distances are not represented here and additional studies would be needed to verify their precision.     

Nonparametric supervised learning by linear interpolation with maximum entropy

Nonparametric neighborhood methods for learning entail estimation of class conditional probabilities based on relative frequencies of samples that are "near-neighbors" of a test point. We propose and explore the behavior of a learning algorithm that uses linear interpolation and the principle of maximum entropy (LIME). We consider some theoretical properties of the LIME algorithm: LIME weights have exponential form; the estimates are consistent; and the estimates are robust to additive noise. In relation to bias reduction, we show that near-neighbors contain a test point in their convex hull asymptotically. The common linear interpolation solution used for regression on grids or look-up-tables is shown to solve a related maximum entropy problem. LIME simulation results support use of the method, and performance on a pipeline integrity classification problem demonstrates that the proposed algorithm has practical value.     

TreePlus: interactive exploration of networks with enhanced tree layouts

Despite extensive research, it is still difficult to produce effective interactive layouts for large graphs. Dense layout and occlusion make food Webs, ontologies and social networks difficult to understand and interact with. We propose a new interactive visual analytics component called TreePlus that is based on a tree-style layout. TreePlus reveals the missing graph structure with visualization and interaction while maintaining good readability. To support exploration of the local structure of the graph and gathering of information from the extensive reading of labels, we use a guiding metaphor of "plant a seed and watch it grow." It allows users to start with a node and expand the graph as needed, which complements the classic overview techniques that can be effective at (but often limited to) revealing clusters. We describe our design goals, describe the interface and report on a controlled user study with 28 participants comparing TreePlus with a traditional graph interface for six tasks. In general, the advantage of TreePlus over the traditional interface increased as the density of the displayed data increased. Participants also reported higher levels of confidence in their answers with TreePlus and most of them preferred TreePlus     

Unsupervised multiresolution segmentation for images with low depthof field

Unsupervised segmentation of images with low depth of field (DOF) is highly useful in various applications. This paper describes a novel multiresolution image segmentation algorithm for low DOF images. The algorithm is designed to separate a sharply focused object-of-interest from other foreground or background objects. The algorithm is fully automatic in that all parameters are image independent. A multi-scale approach based on high frequency wavelet coefficients and their statistics is used to perform context-dependent classification of individual blocks of the image. Unlike other edge-based approaches, our algorithm does not rely on the process of connecting object boundaries. The algorithm has achieved high accuracy when tested on more than 100 low DOF images, many with inhomogeneous foreground or background distractions. Compared with he state of the art algorithms, this new algorithm provides better accuracy at higher speed     

Photocatalytic effect of carbon-modified n-TiO2 nanoparticles under visible light illumination

The present research focused on wet process synthesis of visible light active carbon-modified (CM)-n-TiO₂ nanoparticles and their photocatalytic activity. The CM-n-TiO₂ was synthesized by hydrolysis of TiCl₄ in the presence of tetrabutylammonium hydroxide and also in the presence of glucose and sodium hydroxide. UV–vis spectra, X-ray diffraction (XRD), and FT-IR were used to characterize these photocatalysts. It was found that the CM-n-TiO₂ nanoparticles synthesized by hydrolysis with tetrabutylammonium hydroxide or with sodium hydroxide and glucose when subjected to extended aging and subsequent calcinations absorb well into the visible to near infrared region up to 800 nm and exhibit enhanced visible light photocatalytic activity on degradation of 4-chlorophenol. CM-n-TiO₂ synthesized using glucose as the carbon source generated 13-fold increase in the initial rate of photodegradation of 4-chlorophenol compared to those by regular n-TiO₂, whereas, it increased only eight-fold when tetrabutylammonium hydroxide was used as the carbon source.     

Load combination analysis by ‘Directional simulation in the load space’

The reliability of structures subjected to multiple time-varying random loads is considered herein. It is well-known that the reliability of such systems may be evaluated by considering outcrossings of the load process vector out of a safe domain, and the contribution of individual loads to structural failure may be evaluated by considering outcrossings caused by combinations of one or more loads. In this paper the ‘Directional Simulation in the Load Space’ approach to reliability analysis is developed to consider explicitly outcrossings caused by all possible combinations of loads, during analysis of systems comprising stationary continuous Gaussian loads. To do this, the direction of the load process vector is ‘fixed’ at each point of outcrossing (to physically represent the particular combination of loads causing the outcrossing), and, by considering each possible load combination, all loads not causing an outcrossing are then held constant during radial integration (to model correctly that they do not contribute to each outcrossing). A numerical example demonstrating the validity of the proposed formulation is presented.     

Involvement of Ventral Pallidum in Prefrontal Cortex-Dependent Aspects of Spatial Working Memory.

Ventral pallidum (VP) is an important source of limbic input to medial thalamus. Three studies examined the role of VP in spatial memory tasks impaired by medial thalamic lesions. In the 1st study, rats with VP lesions were impaired performing delayed matching trained with retractable levers (DMRL), a measure sensitive to prefrontal (but not hippocampal) damage. The 2nd study demonstrated dose-dependent DMRL impairment following microinjection of gamma-aminobutyric acidA, glutamate, or mu-opioid agonists in VP. In the 3rd study, VP lesions had no effect on varying choice radial-maze delayed nonmatching, a measure sensitive to hippocampal (but not prefrontal) lesions. These results suggest a common role in spatial memory for VP and other components of prefrontal-ventral striatopallidothalamic circuits distinct from hippocampal function. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Stress Generation, Avoidance Coping, and Depressive Symptoms: A 10-Year Model.

This study examined (a) the role of avoidance coping in prospectively generating both chronic and acute life stressors and (b) the stress-generating role of avoidance coping as a prospective link to future depressive symptoms. Participants were 1,211 late-middle-aged individuals (500 women and 711 men) assessed 3 times over a 10-year period. As predicted, baseline avoidance coping was prospectively associated with both more chronic and more acute life stressors 4 years later. Furthermore, as predicted, these intervening life stressors linked baseline avoidance coping and depressive symptoms 10 years later, controlling for the influence of initial depressive symptoms. These findings broaden knowledge about the stress-generation process and elucidate a key mechanism through which avoidance coping is linked to depressive symptoms. (PsycINFO Database Record (c) 2010 APA, all rights reserved)