Adversarial attacks hidden in plain sight

Convolutional neural networks have been used to achieve a string of successes during recent years, but their lack of interpretability remains a serious issue. Adversarial examples are designed to deliberately fool neural networks into making any desired incorrect classification, potentially with very high certainty. Several defensive approaches increase robustness against adversarial attacks, demanding attacks of greater magnitude, which lead to visible artifacts. By considering human visual perception, we compose a technique that allows to hide such adversarial attacks in regions of high complexity, such that they are imperceptible even to an astute observer. We carry out a user study on classifying adversarially modified images to validate the perceptual quality of our approach and find significant evidence for its concealment with regards to human visual perception

Nonparametric Competitors to the Two-Way ANOVA

↵LARRY E. TOOTHAKER is David Ross Boyd Professor of Psychology at the University of Oklahoma, Norman, OK 73019. He specializes in robustness of ANOVA, including repeated measures designs, multiple comparison procedures, and nonparametrics.Yeshttps://us.sagepub.com/en-us/nam/manuscript-submission-guideline

Near-Infrared Spectroscopy Measured Cerebral Blood Flow from Spontaneous Oxygenation Changes in Neonatal Brain Injury

Neonates with hypoxic-ischaemic (HI) brain injury were monitored using a broadband near-infrared spectroscopy (NIRS) system in the neonatal intensive care unit. The aim of this work is to use the NIRS cerebral oxygenation data (HbD = oxygenated-haemoglobin - deoxygenated-haemoglobin) combined with arterial saturation (SaO2) from pulse oximetry to calculate cerebral blood flow (CBF) based on the oxygen swing method, during spontaneous desaturation episodes. The method is based on Fick's principle and uses HbD as a tracer; when a sudden change in SaO2 occurs, the change in HbD represents a change in tracer concentration, and thus it is possible to estimate CBF. CBF was successfully calculated with broadband NIRS in 11 HIE infants (3 with severe injury) for 70 oxygenation events on the day of birth. The average CBF was 18.0 ± 12.7 ml 100 g-1 min-1 with a range of 4 ml 100 g-1 min-1 to 60 ml 100 g-1 min-1. For infants with severe HIE (as determined by magnetic resonance spectroscopy) CBF was significantly lower (p = 0.038, d = 1.35) than those with moderate HIE on the day of birth

Temporal variables and personal factors in glare sensation

Previous laboratory experiments have provided evidence of an effect of time of day on glare sensation. During the tests, temporal variables and personal factors were also measured to analyse their influence on levels of visual discomfort as the day progresses. The results revealed statistically significant and practically relevant tendencies towards greater tolerance to source luminance from artificial lighting at all times of day for earlier chronotypes and for participants not having ingested caffeine. No conclusive evidence was found for the effect of fatigue, sky condition and prior light exposure on glare sensation throughout the day. These findings suggest that temporal variables and personal factors should be measured in conjunction with visual discomfort levels to explore the causes of the wide individual differences commonly associated with the subjective evaluation of glare sensation

Semantic similarity scales : Using semantic similarity scales to measure depression and worry

The aims of this chapter include describing: how the semantic representations may be used to measure the semantic similarity between words. the validity of semantic similarity as measured by cosine. how semantic similarity scales can be used in research. how to apply t-test to compare two sets of texts using semantic similarity (i.e. “semantic” t-test). how to visualize the word responses by plotting words according to semantic similarity scales. a research study where depression is measured using semantic similarity scales, independent from traditional rating scales. This chapter describes how semantic representations based on Latent Semantic Analysis (LSA; Landauer and Dumais 1997) may be used to measure the semantic similarity between two words, sets of words or texts. Whereas Nielsen and Hansen describe how to create semantic representations in Chap. 1; this chapter focuses on describing how these may be used in research to estimate how similar words/texts are in meaning as well as testing whether two sets of words statistically differ. This approach may, for example, be used to detect between group differences in an experimental design. First, we describe how a single word’s semantic representation may be added together to describe the meaning of several words or an entire text. Second, we discuss how to measure semantic similarity using cosine of the angle of the words’ position in the semantic space. Third, we describe how this procedure of text quantification makes it possible for researchers to use statistical tests (e.g., semantic t-test) for investigating, for example, differences between freely generated narratives. Lastly, we carry out a research study building on studies by Kjell et al. (2018) that demonstrated that semantic similarity scales may be used to measure, differentiate and describe psychological constructs, including depression and worry, independent from traditional numerical rating scales