Analysis and detection of cognitive load and frustration in drivers' speech

Non-driving related cognitive load and variations of emotional state may impact a driver’s capability to control a vehicle and introduces driving errors. Availability of reliable cognitive load and emotion detection in drivers would benefit the design of active safety systems and other intelligent in-vehicle interfaces. In this study, speech produced by 68 subjects while driving in urban areas is analyzed. A particular focus is on speech production differences in two secondary cognitive tasks, interactions with a co-driver and calls to automated spoken dialog systems (SDS), and two emotional states during the SDS interactions - neutral/negative. A number of speech parameters are found to vary across the cognitive/emotion classes. Suitability of selected cepstral- and production-based features for automatic cognitive task/emotion classification is investigated. A fusion of GMM/SVM classifiers yields an accuracy of 94.3% in cognitive task and 81.3% in emotion classification.

The delta-phase spectrum with application to voice activity detection and speaker recognition

For several reasons, the Fourier phase domain is less favored than the magnitude domain in signal processing and modeling of speech. To correctly analyze the phase, several factors must be considered and compensated, including the effect of the step size, windowing function and other processing parameters. Building on a review of these factors, this paper investigates a spectral representation based on the Instantaneous Frequency Deviation, but in which the step size between processing frames is used in calculating phase changes, rather than the traditional single sample interval. Reflecting these longer intervals, the term delta-phase spectrum is used to distinguish this from instantaneous derivatives. Experiments show that mel-frequency cepstral coefficients features derived from the delta-phase spectrum (termed Mel-Frequency delta-phase features) can produce broadly similar performance to equivalent magnitude domain features for both voice activity detection and speaker recognition tasks. Further, it is shown that the fusion of the magnitude and phase representations yields performance benefits over either in isolation.

High-Order Concept Associations Mining and Inferential Language Modeling for Online Review Spam Detection

Despite many incidents about fake online consumer reviews have been reported, very few studies have been conducted to date to examine the trustworthiness of online consumer reviews. One of the reasons is the lack of an effective computational method to separate the untruthful reviews (i.e., spam) from the legitimate ones (i.e., ham) given the fact that prominent spam features are often missing in online reviews. The main contribution of our research work is the development of a novel review spam detection method which is underpinned by an unsupervised inferential language modeling framework. Another contribution of this work is the development of a high-order concept association mining method which provides the essential term association knowledge to bootstrap the performance for untruthful review detection. Our experimental results confirm that the proposed inferential language model equipped with high-order concept association knowledge is effective in untruthful review detection when compared with other baseline methods.

The use of semantic priming as a paradigm for the detection of faking on self-report psychological tests

It is recognised that individuals do not always respond honestly when completing psychological tests. One of the foremost issues for research in this area is the inability to detect individuals attempting to fake. While a number of strategies have been identified in faking, a commonality of these strategies is the latent role of long term memory. Seven studies were conducted in order to examine whether it is possible to detect the activation of faking related cognitions using a lexical decision task. Study 1 found that engagement with experiential processing styles predicted the ability to fake successfully, confirming the role of associative processing styles in faking. After identifying appropriate stimuli for the lexical decision task (Studies 2A and 2B), Studies 3 to 5 examined whether a cognitive state of faking could be primed and subsequently identified, using a lexical decision task. Throughout the course of these studies, the experimental methodology was increasingly refined in an attempt to successfully identify the relevant priming mechanisms. The results were consistent and robust throughout the three priming studies: faking good on a personality test primed positive faking related words in the lexical decision tasks. Faking bad, however, did not result in reliable priming of negative faking related cognitions. To more completely address potential issues with the stimuli and the possible role of affective priming, two additional studies were conducted. Studies 6A and 6B revealed that negative faking related words were more arousing than positive faking related words, and that positive faking related words were more abstract than negative faking related words and neutral words. Study 7 examined whether the priming effects evident in the lexical decision tasks occurred as a result of an unintentional mood induction while faking the psychological tests. Results were equivocal in this regard. This program of research aligned the fields of psychological assessment and cognition to inform the preliminary development and validation of a new tool to detect faking. Consequently, an implicit technique to identify attempts to fake good on a psychological test has been identified, using long established and robust cognitive theories in a novel and innovative way. This approach represents a new paradigm for the detection of individuals responding strategically to psychological testing. With continuing development and validation, this technique may have immense utility in the field of psychological assessment.

Comparison of PCR, nested PCR, and random amplified polymorphic DNA PCR for detection and typing of Ureaplasma urealyticum in specimens from pregnant women

A PCR assay, using three primer pairs, was developed for the detection of Ureaplasma urealyticum, parvo biovar, mba types 1, 3, and 6, in cultured clinical specimens. The primer pairs were designed by using the polymorphic base positions within a 310- to 311-bp fragment of the 5* end and upstream control region of the mba gene. The specificity of the assay was confirmed with reference serovars 1, 3, 6, and 14 and by the amplified-fragment sizes (81 bp for mba 1, 262 bp for mba 3, and 193 bp for mba 6). A more sensitive nested PCR was also developed. This involved a first-step PCR, using the primers UMS-125 and UMA226, followed by the nested mba-type PCR described above. This nested PCR enabled the detection and typing of small numbers of U. urealyticum cells, including mixtures, directly in original clinical specimens. By using random amplified polymorphic DNA (RAPD) PCR with seven arbitrary primers, we were also able to differentiate the two biovars of U. urealyticum and to identify 13 RAPD-PCR subtypes. By applying these subtyping techniques to clinical samples collected from pregnant women, we established that (i) U. urealyticum is often a persistent colonizer of the lower genital tract from early midtrimester until the third trimester of pregnancy, (ii) mba type 6 was isolated significantly more often (P 5 0.048) from women who delivered preterm than from women who delivered at term, (iii) no particular ureaplasma subtype(s) was associated with placental infections and/or adverse pregnancy outcomes, and (iv) the ureaplasma subtypes most frequently isolated from women were the same subtypes most often isolated from infected placentas.

Mid-gestation intra-amniotic infection with Ureaplasma parvum is resolved within spiny mice (Acomys cahirinus) by term delivery: but caused chronic infection of fetal lungs and placentae

Background: Ureaplasma species in amniotic fluid at the time of second-trimester amniocentesis increases the risk of preterm birth, but most affected pregnancies continue to term (Gerber et al. J Infect Dis 2003). We aimed to model intra-amniotic (IA) ureaplasma infection in spiny mice, a species with a relatively long gestation (39 days) that allows investigation of the disposition and possible clearance of ureaplasmas in the feto-placental compartment. Method: Pregnant spiny mice received IA injections of U. parvum serovar 6 (10µL, 1x104 colony-forming-units in PBS) or 10B media (10µL; control) at 20 days (d) of gestation (term=39d). At 37d fetuses (n=3 ureaplasma, n=4 control) were surgically delivered and tissues were collected for; bacterial culture, ureaplasma mba and urease gene expression by PCR, tissue WBC counts and indirect fluorescent antibody (IFA) staining using anti-ureaplasma serovar 6 (rabbit) antiserum. Maternal and fetal plasma IgG was measured by Western blot. Results: Ureaplasmas were not detected by culture or PCR in fetal or maternal tissues but were visualized by IFA within placental and fetal lung tissues, in association with inflammatory changes and elevated WBC counts (p<0.0001). Anti-ureaplasma IgG was detected in maternal (2/2 tested) and fetal (1/2 tested) plasma but not in controls (0/3). Conclusions: IA injection of ureaplasmas in mid-gestation spiny mice caused persistent fetal lung and placental infection even though ureaplasmas were undetectable using standard culture or PCR techniques. This is consistent with resolution of IA infection, which may occur in human pregnancies that continue to term despite detection of ureaplasmas in mid-gestation.

Novelty-based visual obstacle detection in agriculture

This paper describes a novel obstacle detection system for autonomous robots in agricultural field environments that uses a novelty detector to inform stereo matching. Stereo vision alone erroneously detects obstacles in environments with ambiguous appearance and ground plane such as in broad-acre crop fields with harvested crop residue. The novelty detector estimates the probability density in image descriptor space and incorporates image-space positional understanding to identify potential regions for obstacle detection using dense stereo matching. The results demonstrate that the system is able to detect obstacles typical to a farm at day and night. This system was successfully used as the sole means of obstacle detection for an autonomous robot performing a long term two hour coverage task travelling 8.5 km.

Do spoken nonword and sentence repetition tasks discriminate language impairment in children with an ASD?

The primary aim of this paper was to investigate heterogeneity in language abilities of children with a confirmed diagnosis of an ASD (N = 20) and children with typical development (TD; N = 15). Group comparisons revealed no differences between ASD and TD participants on standard clinical assessments of language ability, reading ability or nonverbal intelligence. However, a hierarchical cluster analysis based on spoken nonword repetition and sentence repetition identified two clusters within the combined group of ASD and TD participants. The first cluster (N = 6) presented with significantly poorer performances than the second cluster (N = 29) on both of the clustering variables in addition to single word and nonword reading. The significant differences between the two clusters occur within a context of Cluster 1 having language impairment and a tendency towards more severe autistic symptomatology. Differences between the oral language abilities of the first and second clusters are considered in light of diagnosis, attention and verbal short term memory skills and reading impairment.

Automatic UAV forced landing site detection using machine learning

The commercialization of aerial image processing is highly dependent on the platforms such as UAVs (Unmanned Aerial Vehicles). However, the lack of an automated UAV forced landing site detection system has been identified as one of the main impediments to allow UAV flight over populated areas in civilian airspace. This article proposes a UAV forced landing site detection system that is based on machine learning approaches including the Gaussian Mixture Model and the Support Vector Machine. A range of learning parameters are analysed including the number of Guassian mixtures, support vector kernels including linear, radial basis function Kernel (RBF) and polynormial kernel (poly), and the order of RBF kernel and polynormial kernel. Moreover, a modified footprint operator is employed during feature extraction to better describe the geometric characteristics of the local area surrounding a pixel. The performance of the presented system is compared to a baseline UAV forced landing site detection system which uses edge features and an Artificial Neural Network (ANN) region type classifier. Experiments conducted on aerial image datasets captured over typical urban environments reveal improved landing site detection can be achieved with an SVM classifier with an RBF kernel using a combination of colour and texture features. Compared to the baseline system, the proposed system provides significant improvement in term of the chance to detect a safe landing area, and the performance is more stable than the baseline in the presence of changes to the UAV altitude.

Support for an auto-associative model of spoken cued recall: Evidence from fMRI

Cued recall and item recognition are considered the standard episodic memory retrieval tasks. However, only the neural correlates of the latter have been studied in detail with fMRI. Using an event-related fMRI experimental design that permits spoken responses, we tested hypotheses from an auto-associative model of cued recall and item recognition [Chappell, M., & Humphreys, M. S. (1994). An auto-associative neural network for sparse representations: Analysis and application to models of recognition and cued recall. Psychological Review, 101, 103-128]. In brief, the model assumes that cues elicit a network of phonological short term memory (STM) and semantic long term memory (LTM) representations distributed throughout the neocortex as patterns of sparse activations. This information is transferred to the hippocampus which converges upon the item closest to a stored pattern and outputs a response. Word pairs were learned from a study list, with one member of the pair serving as the cue at test. Unstudied words were also intermingled at test in order to provide an analogue of yes/no recognition tasks. Compared to incorrectly rejected studied items (misses) and correctly rejected (CR) unstudied items, correctly recalled items (hits) elicited increased responses in the left hippocampus and neocortical regions including the left inferior prefrontal cortex (LIPC), left mid lateral temporal cortex and inferior parietal cortex, consistent with predictions from the model. This network was very similar to that observed in yes/no recognition studies, supporting proposals that cued recall and item recognition involve common rather than separate mechanisms.

Statistical power calculation and sample size determination for environmental studies with data below detection limits

Power calculation and sample size determination are critical in designing environmental monitoring programs. The traditional approach based on comparing the mean values may become statistically inappropriate and even invalid when substantial proportions of the response values are below the detection limits or censored because strong distributional assumptions have to be made on the censored observations when implementing the traditional procedures. In this paper, we propose a quantile methodology that is robust to outliers and can also handle data with a substantial proportion of below-detection-limit observations without the need of imputing the censored values. As a demonstration, we applied the methods to a nutrient monitoring project, which is a part of the Perth Long-Term Ocean Outlet Monitoring Program. In this example, the sample size required by our quantile methodology is, in fact, smaller than that by the traditional t-test, illustrating the merit of our method.

Detection of Helicobacter pullorum in meat chickens in Australia

The bacterial genus Helicobacter is a member of the Campylobacteriales in the Epsilonproteobacteria subphylum, and is comprised of organisms that are morphologically similar to Campylobacter. The term ‘campylobacteria’ is used to encompass the genera Campylobacter, Arcobacter, Helicobacter and Anaerobiospirillum. Helicobacter was separated from the genus Campylobacter in 1989. Helicobacter spp have been isolated from gastric tissue or intestinal contents of humans and a wide range of animal species, with some associated with disease...

Short- and long-term movement patterns in the freshwater whipray (Himantura dalyensis) determined by the signal processing of passive acoustic telemetry data.

Patterns of movement in aquatic animals reflect ecologically important behaviours. Cyclical changes in the abiotic environment influence these movements, but when multiple processes occur simultaneously, identifying which is responsible for the observed movement can be complex. Here we used acoustic telemetry and signal processing to define the abiotic processes responsible for movement patterns in freshwater whiprays (Himantura dalyensis). Acoustic transmitters were implanted into the whiprays and their movements detected over 12 months by an array of passive acoustic receivers, deployed throughout 64 km of the Wenlock River, Qld, Australia. The time of an individual's arrival and departure from each receiver detection field was used to estimate whipray location continuously throughout the study. This created a linear-movement-waveform for each whipray and signal processing revealed periodic components within the waveform. Correlation of movement periodograms with those from abiotic processes categorically illustrated that the diel cycle dominated the pattern of whipray movement during the wet season, whereas tidal and lunar cycles dominated during the dry season. The study methodology represents a valuable tool for objectively defining the relationship between abiotic processes and the movement patterns of free-ranging aquatic animals and is particularly expedient when periods of no detection exist within the animal location data.

The Effect of Intrauterine Growth Restriction on Long-Term Outcome in Very or Extremely Low Birth Weight Infants

The project consisted of two long-term follow-up studies of preterm children addressing the question whether intrauterine growth restriction affects the outcome. Assessment at 5 years of age of 203 children with a birth weight less than 1000 g born in Finland in 1996-1997 showed that 9% of the children had cognitive impairment, 14% cerebral palsy, and 4% needed a hearing aid. The intelligence quotient was lower (p<0.05) than the reference value. Thus, 20% exhibited major, 19% minor disabilities, and 61% had no functional abnormalities. Being small for gestational age (SGA) was associated with sub-optimal growth later. In children born before 27 gestational weeks, the SGA had more neuropsychological disabilities than those appropriate for gestational age (AGA). In another cohort with birth weight less than 1500 g assessed at 5 years of age, echocardiography showed a thickened interventricular septum and a decreased left ventricular end-diastolic diameter in both SGA and AGA born children. They also had a higher systolic blood pressure than the reference. Laser-Doppler flowmetry showed different endothelium-dependent and -independent vasodilation responses in the AGA children compared to those of the controls. SGA was not associated with cardio-vascular abnormalities. Auditory event-related potentials (AERPs) were recorded using an oddball paradigm with frequency deviants (standard tone 500 Hz and deviant 750-Hz with 10% probability). At term, the P350 was smaller in SGA and AGA infants than in controls. At 12 months, the automatic change detection peak (mismatch negativity, MMN) was observed in the controls. However, the pre-term infants had a difference positivity that correlated with their neurodevelopment scores. At 5 years of age, the P1-deflection, which reflects primary auditory processing, was smaller, and the MMN larger in the preterm than in the control children. Even with a challenging paradigm or a distraction paradigm, P1 was smaller in the preterm than in the control children. The SGA and AGA children showed similar AERP responses. Prematurity is a major risk factor for abnormal brain development. Preterm children showed signs of cardiovascular abnormality suggesting that prematurity per se may carry a risk for later morbidity. The small positive amplitudes in AERPs suggest persisting altered auditory processing in the preterm in-fants.

Detection and progression of chronic allograft nephropathy : A study based on protocol biopsies

Background. Kidney transplantation (KTX) is considered to be the best treatment of terminal uremia. Despite improvements in short-term graft survival, a considerable number of kidney allografts are lost due to the premature death of patients with a functional kidney and to chronic allograft nephropathy (CAN). Aim. To investigate the risk factors involved in the progression of CAN and to analyze diagnostic methods for this entity. Materials and methods. Altogether, 153 implant and 364 protocol biopsies obtained between June 1996 and April 2008 were analyzed. The biopsies were classified according to Banff ’97 and chronic allograft damage index (CADI). Immunohistochemistry for TGF-β1 was performed in 49 biopsies. Kidney function was evaluated by creatinine and/or cystatin C measurement and by various estimates of glomerular filtration rate (GFR). Demographic data of the donors and recipients were recorded after 2 years’ follow-up. Results. Most of the 3-month biopsies (73%) were nearly normal. The mean CADI score in the 6-month biopsies decreased significantly after 2001. Diastolic hypertension correlated with ΔCADI. Serum creatinine concentration at hospital discharge and glomerulosclerosis were risk factors for ΔCADI. High total and LDL cholesterol, low HDL and hypertension correlated with chronic histological changes. The mean age of the donors increased from 41 -52 years. Older donors were more often women who had died from an underlying disease. The prevalence of delayed graft function increased over the years, while acute rejections (AR) decreased significantly over the years. Sub-clinical AR was observed in 4% and it did not affect long-term allograft function or CADI. Recipients´ drug treatment was modified along the Studies, being mycophenolate mophetil, tacrolimus, statins and blockers of the renine-angiotensin-system more frequently prescribed after 2001. Patients with a higher ΔCADI had lower GFR during follow-up. CADI over 2 was best predicted by creatinine, although with modest sensitivity and specificity. Neither cystatin C nor other estimates of GFR were superior to creatinine for CADI prediction. Cyclosporine A toxicity was seldom seen. Low cyclosporin A concentration after 2 h correlated with TGF- β1 expression in interstitial inflammatory cells, and this predicted worse graft function. Conclusions. The progression of CAN has been affected by two major factors: the donors’ characteristics and the recipients’ hypertension. The increased prevalence of DGF might be a consequence of the acceptance of older donors who had died from an underlying disease. Implant biopsies proved to be of prognostic value, and they are essential for comparison with subsequent biopsies. The progression of histological damage was associated with hypertension and dyslipidemia. The augmented expression of TGF-β1 in inflammatory cells is unclear, but it may be related to low immunosuppression. Serum creatinine is the most suitable tool for monitoring kidney allograft function on every-day basis. However, protocol biopsies at 6 and 12 months predicted late kidney allograft dysfunction and affected the clinical management of the patients. Protocol biopsies are thus a suitable surrogate to be used in clinical trials and for monitoring kidney allografts.