Detecting Lies and Truths in Social Work: How Suspicion Level and Familiarity Affect Detection Accuracy

2012 ◽  
Vol 44 (2) ◽  
pp. 328-347
Author(s):  
M.-A. Reinhard ◽  
T. Marksteiner ◽  
R. Schindel ◽  
O. Dickhauser
Keyword(s):  
Social Work ◽  
Detection Accuracy ◽  
Affect Detection

scholarly journals Effects of Element Type and Spatial Grouping on Symmetry Detection

Perception ◽  
1993 ◽  
Vol 22 (5) ◽  
pp. 565-587 ◽  
Author(s):  
Paul J Locher ◽  
Johan Wagemans
Keyword(s):  
Mirror Symmetry ◽  
Symmetry Axis ◽  
Detection Accuracy ◽  
Axis Orientation ◽  
Primal Sketch ◽  
Affect Detection ◽  
Spatial Grouping ◽  
Element Type ◽  
Random Patterns ◽  
Advance Knowledge

The influence of local and global attributes of symmetric patterns on the perceptual salience of symmetry was investigated. After tachistoscopic viewing, subjects discriminated between symmetric and either random patterns (experiment 1) or their perturbed counterparts (experiment 2) created by replacing one third of the mirror element-pairs of symmetric stimuli with ‘random’ elements. In general, it was found that perceptibility of symmetry, measured by response time and detection accuracy, was not influenced in a consistent way by type of pattern element (dots or line segments oriented vertically, horizontally, obliquely, or in all three orientations about the symmetry axis). Nor did axis orientation (vertical, horizontal, oblique), advance knowledge of axis orientation, practice effects, or subject sophistication differentially affect detection. A highly salient global percept of symmetry emerged, on the other hand, when elements were clustered together within a pattern, or grouped in symmetric pairs along a single symmetry axis or two orthogonal axes. Results suggest that mirror symmetry is detected preattentively, presumably by some kind of integral code which emerges from the interaction between display elements and the way they are organized spatially. It is proposed that symmetry is coded and signalled by the same spatial grouping processes as those responsible for construction of the full primal sketch.

scholarly journals An Intra-Subject Approach Based on the Application of HMM to Predict Concentration in Educational Contexts from Nonintrusive Physiological Signals in Real-World Situations

Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1777
Author(s):  
Ana Serrano-Mamolar ◽  
Miguel Arevalillo-Herráez ◽  
Guillermo Chicote-Huete ◽  
Jesus G. G. Boticario
Keyword(s):  
Real World ◽  
Markov Models ◽  
Low Cost ◽  
High Accuracy ◽  
Physiological Signals ◽  
Detection Accuracy ◽  
Related Information ◽  
Additional Control ◽  
Affect Detection ◽  
Potential Use

Previous research has proven the strong influence of emotions on student engagement and motivation. Therefore, emotion recognition is becoming very relevant in educational scenarios, but there is no standard method for predicting students’ affects. However, physiological signals have been widely used in educational contexts. Some physiological signals have shown a high accuracy in detecting emotions because they reflect spontaneous affect-related information, which is fresh and does not require additional control or interpretation. Most proposed works use measuring equipment for which applicability in real-world scenarios is limited because of its high cost and intrusiveness. To tackle this problem, in this work, we analyse the feasibility of developing low-cost and nonintrusive devices to obtain a high detection accuracy from easy-to-capture signals. By using both inter-subject and intra-subject models, we present an experimental study that aims to explore the potential application of Hidden Markov Models (HMM) to predict the concentration state from 4 commonly used physiological signals, namely heart rate, breath rate, skin conductance and skin temperature. We also study the effect of combining these four signals and analyse their potential use in an educational context in terms of intrusiveness, cost and accuracy. The results show that a high accuracy can be achieved with three of the signals when using HMM-based intra-subject models. However, inter-subject models, which are meant to obtain subject-independent approaches for affect detection, fail at the same task.

Comment on: “root orientation can affect detection accuracy of ground-penetrating radar”

2014 ◽  
Vol 380 (1-2) ◽  
pp. 441-444 ◽  
Author(s):  
Yuan Wu ◽  
Li Guo ◽  
Wentao Li ◽  
Xihong Cui ◽  
Jin Chen
Keyword(s):  
Ground Penetrating Radar ◽  
Detection Accuracy ◽  
Affect Detection ◽  
Ground Penetrating ◽  
Root Orientation

Research on Definition Evaluation Algorithm of the Test Equipments for Phoropters

2013 ◽  
Vol 552 ◽  
pp. 428-433
Author(s):  
Fei Li ◽  
Lin Li ◽  
Bao Yu Hong ◽  
Wen Li Liu
Keyword(s):  
Detection Accuracy ◽  
Evaluation Function ◽  
Evaluation Functions ◽  
Affect Detection ◽  
Evaluation Algorithm ◽  
Image Acquisition System ◽  
Fuzzy Degree ◽  
Defocused Image ◽  
The Mathematical Model ◽  
Automatic Focusing

Definition evaluation function of the defocused image is key to realize automatic focusing. Sensitivity of the definition evaluation function can directly affect detection accuracy of the test equipments. By analyzing and summarizing the definition evaluation functions which are commonly used currently, we will present a new image definition evaluation function based on wavelet transform in this paper. On the basis of theoretical analysis, we also establish and analyze the mathematical model of evaluation function, and make experiment and simulation with the series of images collected by the video image acquisition system. Compared with the traditional definition evaluation function, the definition evaluation function of defocused image defined by this algorithm is more sensitive to fuzzy degree of image and more consistent with human subjective vision.

Root orientation can affect detection accuracy of ground-penetrating radar

2013 ◽  
Vol 373 (1-2) ◽  
pp. 317-327 ◽  
Author(s):  
Toko Tanikawa ◽  
Yasuhiro Hirano ◽  
Masako Dannoura ◽  
Keitarou Yamase ◽  
Kenji Aono ◽  
...  
Keyword(s):  
Ground Penetrating Radar ◽  
Detection Accuracy ◽  
Affect Detection ◽  
Ground Penetrating ◽  
Root Orientation

scholarly journals Constrained sampling experiments reveal principles of detection in natural scenes

2017 ◽  
Vol 114 (28) ◽  
pp. E5731-E5740 ◽  
Author(s):  
Stephen Sebastian ◽  
Jared Abrams ◽  
Wilson S. Geisler
Keyword(s):  
Luminance Contrast ◽  
Gain Factor ◽  
Three Dimensions ◽  
Natural Image ◽  
Detection Accuracy ◽  
Natural Scenes ◽  
Detection Thresholds ◽  
Vision Science ◽  
Affect Detection ◽  
Major Factors

A fundamental everyday visual task is to detect target objects within a background scene. Using relatively simple stimuli, vision science has identified several major factors that affect detection thresholds, including the luminance of the background, the contrast of the background, the spatial similarity of the background to the target, and uncertainty due to random variations in the properties of the background and in the amplitude of the target. Here we use an experimental approach based on constrained sampling from multidimensional histograms of natural stimuli, together with a theoretical analysis based on signal detection theory, to discover how these factors affect detection in natural scenes. We sorted a large collection of natural image backgrounds into multidimensional histograms, where each bin corresponds to a particular luminance, contrast, and similarity. Detection thresholds were measured for a subset of bins spanning the space, where a natural background was randomly sampled from a bin on each trial. In low-uncertainty conditions, both the background bin and the amplitude of the target were fixed, and, in high-uncertainty conditions, they varied randomly on each trial. We found that thresholds increase approximately linearly along all three dimensions and that detection accuracy is unaffected by background bin and target amplitude uncertainty. The results are predicted from first principles by a normalized matched-template detector, where the dynamic normalizing gain factor follows directly from the statistical properties of the natural backgrounds. The results provide an explanation for classic laws of psychophysics and their underlying neural mechanisms.

Reply to: “Comment on root orientation can affect detection accuracy of ground-penetrating radar”

2014 ◽  
Vol 380 (1-2) ◽  
pp. 445-450 ◽  
Author(s):  
Toko Tanikawa ◽  
Masako Dannoura ◽  
Keitarou Yamase ◽  
Hidetoshi Ikeno ◽  
Yasuhiro Hirano
Keyword(s):  
Ground Penetrating Radar ◽  
Detection Accuracy ◽  
Affect Detection ◽  
Ground Penetrating ◽  
Root Orientation

scholarly journals NOVEL HYPERSPECTRAL ANOMALY DETECTION METHODS BASED ON UNSUPERVISED NEAREST REGULARIZED SUBSPACE

2018 ◽  
Vol XLII-3 ◽  
pp. 539-546
Author(s):  
Z. Hou ◽  
Y. Chen ◽  
K. Tan ◽  
P. Du
Keyword(s):  
Anomaly Detection ◽  
Spatial Information ◽  
Hyperspectral Imagery ◽  
Detection Methods ◽  
Detection Accuracy ◽  
Outlier Removal ◽  
Residual Image ◽  
Affect Detection ◽  
Anomaly Detector ◽  
Dual Window

Anomaly detection has been of great interest in hyperspectral imagery analysis. Most conventional anomaly detectors merely take advantage of spectral and spatial information within neighboring pixels. In this paper, two methods of Unsupervised Nearest Regularized Subspace-based with Outlier Removal Anomaly Detector (UNRSORAD) and Local Summation UNRSORAD (LSUNRSORAD) are proposed, which are based on the concept that each pixel in background can be approximately represented by its spatial neighborhoods, while anomalies cannot. Using a dual window, an approximation of each testing pixel is a representation of surrounding data via a linear combination. The existence of outliers in the dual window will affect detection accuracy. Proposed detectors remove outlier pixels that are significantly different from majority of pixels. In order to make full use of various local spatial distributions information with the neighboring pixels of the pixels under test, we take the local summation dual-window sliding strategy. The residual image is constituted by subtracting the predicted background from the original hyperspectral imagery, and anomalies can be detected in the residual image. Experimental results show that the proposed methods have greatly improved the detection accuracy compared with other traditional detection method.

Dysphagia in Hospice Care: The Roles of Social Work and Speech-Language Pathologists

2014 ◽  
Vol 23 (4) ◽  
pp. 173-186 ◽  
Author(s):  
Deborah Hinson ◽  
Aaron J. Goldsmith ◽  
Joseph Murray
Keyword(s):  
Social Work ◽  
End Of Life ◽  
Case Studies ◽  
End Of Life Care ◽  
Hospice Care ◽  
Patient Communication ◽  
Life Care ◽  
Speech Language Pathologists ◽  
Four Levels

This article addresses the unique roles of social work and speech-language pathologists (SLPs) in end-of-life and hospice care settings. The four levels of hospice care are explained. Suggested social work and SLP interventions for end-of-life nutrition and approaches to patient communication are offered. Case studies are used to illustrate the specialized roles that social work and SLP have in end-of-life care settings.

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