False Positive Errors Research Articles

The role of decision making in the over-representation of Aboriginal and Torres Strait Islander children in the Australian child protection system

The over-representation of Aboriginal and Torres Strait Islander children in the child protection is a serious concern in Australia. A proportion of this group of children may have been removed from families who are providing an adequately safe and nurturing environment, reflecting false positive errors in decision making. Assuming this to be true, we draw on the decision-making ecology model of judgement and decision making in child protection to speculate on possible causes of such errors. This model suggests that false positive errors would occur if the level of risk children face is estimated to be higher than is actually the case or if the decision threshold applied in making decisions to remove children from their family are too low. We present a discussion of factors that could potentially influence judgements and decision thresholds, which has implications for practice. It is argued that false positive errors are most likely to be avoided through more thorough and accurate assessments families. When assessing Aboriginal and Torres Strait Islander families, we argue that this requires a theoretically grounded, culturally informed framework to improve assessments and decrease the likelihood of false positive errors in decisions involving Aboriginal and Torres Strait Islander children in Australia.

Feasibility of FT-NIR spectroscopy and Vis/NIR hyperspectral imaging for sorting unsound chestnuts

Authors explored the potential use of Vis/NIR hyperspectral imaging (HSI) and Fourier-transform Near-Infrared (FT-NIR) spectroscopy to be used as in-line tools for the detection of unsound chestnut fruits (i.e. infected and/or infested) in comparison with the traditional sorting technique. For the intended purpose, a total of 720 raw fruits were collected from a local company. Chestnut fruits were preliminarily classified into sound (360 fruits) and unsound (360 fruits) batches using a proprietary floating system at the facility along with manual selection performed by expert workers. The two batches were stored at 4 ± 1 °C until use. Samples were left at ambient temperature for at least 12 h before measurements. Subsequently, fruits were subjected to non-destructive measurements (i.e. spectral analysis) immediately followed by destructive analyses (i.e. microbiological and entomological assays). Classification models were trained using the Partial Least Squares Discriminant Analysis (PLS-DA) by pairing the spectrum of each fruit with the categorical information obtained from its destructive assay (i.e., sound, Y = 0; unsound, Y = 1). Categorical data were also used to evaluate the classification performance of the traditional sorting method. The performance of each PLS-DA model was evaluated in terms of false positive error (FP), false negative error (FN) and total error (TE) rates. The best result (8% FP, 14% FN, 11% TE) was obtained using Savitzky-Golay first derivative with a 5-points window of smoothing on the dataset of raw reflectance spectra scanned from the hilum side of fruit using the Vis/NIR HSI setup. This model showed similarity in terms of False Negative error rate with the best one computed using data from the FT-NIR setup (i.e. 15% FN), which, however, had the lowest global performance (17% TE) due to the highest False Positive error rate (19%). Finally, considering that the total error rate committed by the traditional sorting system was about 14.5% with a tendency of misclassifying unsound fruits, the results indicate the feasibility of a rapid, in-line detection system based on spectroscopic measurements.

Apophenia as the disposition to false positives: A unifying framework for openness and psychoticism.

Positive symptoms of schizophrenia and its extended phenotype-often termed psychoticism or positive schizotypy-are characterized by the inclusion of novel, erroneous mental contents. One promising framework for explaining positive symptoms involves apophenia, conceptualized here as a disposition toward false-positive errors. Apophenia and positive symptoms have shown relations to openness to experience (more specifically, to the openness aspect of the broader openness/intellect domain), and all of these constructs involve tendencies toward pattern seeking. Nonetheless, few studies have investigated the relations between psychoticism and non-self-report indicators of apophenia, let alone the role of normal personality variation. The current research used structural equation models to test associations between psychoticism, openness, intelligence, and non-self-report indicators of apophenia comprising false-positive error rates on a variety of computerized tasks. In Sample 1, 1,193 participants completed digit identification, theory of mind, and emotion recognition tasks. In Sample 2, 195 participants completed auditory signal detection and semantic word association tasks. Psychoticism and the openness aspect were positively correlated. Self-reported psychoticism, openness, and their shared variance were positively associated with apophenia, as indexed by false-positive error rates, whether or not intelligence was controlled for. Apophenia was not associated with other personality traits, and openness and psychoticism were not associated with false-negative errors. Findings provide insights into the measurement of apophenia and its relation to personality and psychopathology. Apophenia and pattern seeking may be promising constructs for unifying the openness aspect of personality with the psychosis spectrum and for providing an explanation of positive symptoms. Results are discussed in the context of possible adaptive characteristics of apophenia as well as potential risk factors for the development of psychotic disorders. (PsycInfo Database Record (c) 2020 APA, all rights reserved).

Commonality in Abnormality: "Abnormal" Neuropsychological Scores Are Common in Baseline Testing of Youth Athletes.

Although research shows that abnormal scores are common in adults, fewer studies examine the frequency of abnormal scores within children. The present study assessed how specific tests, cognitive domains, and patient factors affect the likelihood of abnormally low scores among healthy youth athletes. Youth athletes (n=85, Mage=11.72, SD=2.23) completed a comprehensive neuropsychological battery (N=23 tests) during baseline testing for a sport-related concussion clinical research program. "Abnormal" scores were defined using cutoffs at 1, 1.5, and 2 standard deviations (SDs) below the population mean. Univariate and multivariate Poisson regression models examined abnormal test scores as a function of age, sex, ethnicity, and Full-Scale Intelligence Quotient (FSIQ). Using cutoffs of 1, 1.5, and 2 SDs, the percent of healthy participants with at least one abnormal test score was 61.2, 36.5, and 22.4%, respectively, indicating a high proportion of healthy participants with abnormal scores and low specificity for tests using these cutoffs. In univariate Poisson regression analyses, FSIQ and abnormal score were negatively correlated (p<.001), wherein lower FSIQ was associated with more abnormal scores. In multivariate regression analyses, lower performance on executive function, memory, and processing speed tests was significantly associated with an increased frequency of abnormal scores. Abnormally low scores in healthy youth occur frequently and do not necessarily represent functional deficits. Cutoffs of 2 SDs should be considered for many tests to increase specificity and reduce the risk of false positive errors that could lead to negative clinical outcomes.

Are Established Embedded Performance Validity Test Cut-Offs Generalizable to Patients With Multiple Sclerosis?

Data for the use of embedded performance validity tests (ePVTs) with multiple sclerosis (MS) patients are limited. The purpose of the current study was to determine whether ePVTs previously validated in other neurological samples perform similarly in an MS sample. In this retrospective study, the prevalence of below-criterion responding at different cut-off scores was calculated for each ePVT of interest among patients with MS who passed a stand-alone PVT. Previously established PVT cut-offs generally demonstrated acceptable specificity when applied to our sample. However, the overall cognitive burden of the sample was limited relative to that observed in prior large-scale MS studies. The current study provides initial data regarding the performance of select ePVTs among an MS sample. Results indicate most previously validated cut-offs avoid excessive false positive errors in a predominantly relapsing remitting MS sample. Further validation among MS patients with more advanced disease is warranted.

Reliability of Visual Field testing and its learning curve among children

Abstract Background Vision is a combination of distinct measurable functions including visual acuity, visual field and others. The visual field includes the area of space seen when the individual’s gaze is directed at any central object. Objective The aim of work in our study was to detect the learning curve of visual field testing in a young age group. Methods This study included 72 eyes of 36 children from both sexes aging from 6 to 12 years. All children were from Ain-shams university hospitals outpatient clinics. A detailed questionnaire was answered by both the care-giver and the child on the time spent playing video-games in terms of days per week and hours per day and children were categorized accordingly into VGP and NVGP. Children are devided according to Green and Bavelier’s criteria for VGP participants. The visual field testing was performed twice with a 7-day interval apart and the results of the first session were compared with those of the second. Learning effect was defined as an improvement at results for duration and perimetric reliability indices. Results Mean age of participant children was 8.94 ±1.90. Mean reliability parameters were: 68.25 ± 25.78% of fixation losses in the first perimetric test and 71.02 ± 30.84% in the second, 39.14 ± 28.27% of false positive errors in the first test and 44.01 ± 31.24 in the second, 9.67 ± 5.97% of false negative errors in the first test and 9.22 ± 7.74 in the second. The mean test duration was 6.91 ± 1.78 in the first test and 6.58 ± 1.33 in the second. Conclusion There was remarkable enhancement in the time taken to complete the second perimetric test in VGP with respect to NVGP indicating a better learning curve.

QPMASS: A parallel peak alignment and quantification software for the analysis of large-scale gas chromatography-mass spectrometry (GC-MS)-based metabolomics datasets

Gas chromatography-mass spectrometry (GC-MS) is a robust analytical platform for analysis of small molecules. Recently, it is widely used for large-scale metabolomics studies, in which hundreds or even thousands of samples are analyzed simultaneously, producing a very large and complex GC-MS datasets. A number of software are currently available for processing GC-MS data, but it is too compute-intensive for them to efficiently and accurately align chromatographic peaks from thousands of samples. Here, we report a newly developed software, QPMASS, for analysis of large-scale GC-MS data. The parallel computing with an advanced dynamic programming approach is implemented in QPMASS to align peaks from multiple samples based on retention time and mass spectra, enabling fast processing large-scale datasets. Furthermore, the missing value filtering and backfilling are introduced into the program, greatly reducing false positive and false negative errors to be less than 5%. We demonstrated that it took only 8 h to align and quantify a GC-TOF-MS dataset from 300 rice leaves samples, and 17 h to process a GC-qMS dataset from 1000 rice seed samples by using a personal computer (3.70 GHz CPU, 16 GB of memory and > 100 GB hard disk). QPMASS is written in C++ programming language, and is able to run under Windows operation system with a user-friendly interface.

The Case for Assessing for Negative Response Bias, Not Malingering

ABSTRACT Assessment of response style is a vital component of forensic assessment. However, the focus on malingering in the literature and test offerings has predictable and negative consequences: Because the label of malingering is viewed as toxic, tests designed to assess for it are often long and sacrifice sensitivity to minimize false-positive errors. Rather than focusing on diagnosing or ruling out malingering, examiners and validity test authors can focus on detecting negative response bias that could invalidate other clinical and forensically relevant data. Doing so can avoid overstepping one’s data and stigmatization, allow shorter, more cost-efficient assessments, better inform the court, reduce inherent conflicts of interest, and lessen conflict surrounding the response style issue.

A two-level fusion for building irregularity detection in post-disaster VHR oblique images

A post-disaster scene is more complicated than a vertical pre-disaster one, since it includes debris-covered areas and the imagery angle may be oblique. Detecting buildings and their irregularities in post-disaster oblique images is a valuable resource in many remote sensing applications, such as building damage assessment, which is a critical and challenging task in the disaster management cycle. We proposed a two-level fusion method, which is fast enough to support save and rescue missions. Firstly, an edge-based knowledge-based approach is designed to extract the vertical building map from temporal pre-disaster data. This approach utilizes Gray-Level Co-Occurrence Matrix (GLCM) features and shadow information. The temporal analysis helps the procedure to find stable objects by reducing the effect of noise in fine-resolution data. The mentioned two-level fusion includes data and decision levels. Spectral and georeferenced bands of pre- and post-disaster samples are fused in the data level. The decision-level refers to fusing the results of two classifiers, Spectral-only and Geo-Spectral classifiers. The locality of the method is preserved by the georeferenced feature. The proposed method is implemented on the Google Earth Engine (GEE) platform, and the evaluation is based on Hurricane Nate (2017) and Hurricane Harvey (2017) oblique images. The proposed method shows a significant reduction in the false-positive error, and provides a high performance in detecting irregularity in facade and rooftop areas.

Cyber risks of PMU networks with observation errors: Assessment and mitigation

The PMU network has been utilized to monitor the system operation of a smart grid in recent decades. This brings a significant cyber risk for the power grid as the attacker can manipulate the PMU network to introduce the false positive and false negative observation errors. A novel risk model is proposed for the PMU networks by considering the observation errors. It is discovered that ignoring the due observation errors can severely underestimate the PMU risks as shown by the theoretical and simulation studies. The risk mitigation strategies for improving the safety levels of PMUs with the observation errors are studied. It is found that the optimal mitigation strategy can be different when the observation errors are present. Numerical examples are presented for illustrations as well.

Probability, Presumptions and Evidentiary Burdens in Antitrust Analysis: Revitalizing the Rule of Reason for Exclusionary Conduct

The conservative critique of antitrust law has been highly influential and has facilitated a transformation of antitrust standards of conduct since the 1970s and led to increasingly more permissive standards of conduct. While these changes have taken many forms, all were influenced by the view that competition law was over-deterrent. Critics relied heavily on the assumption that the durability and costs of false positive errors far exceeded those of false negatives. Many of the assumptions that guided this retrenchment of antitrust rules were mistaken and advances in the law and in economic analysis have rendered them anachronistic, particularly with respect to exclusionary conduct. Continued reliance on what are now exaggerated fears of “false positives,” and failure adequately to consider the harm from “false negatives,” has led courts to impose excessive demands of proof on plaintiffs that belie both established procedural norms and sound economic analysis. The result is not better and more reasonable antitrust standards, but instead an embedded ideological preference for non-intervention that creates a tendency toward false negatives, particularly in modern markets characterized by economies of scale and network effects. In this article, we explain how these erroneous assumptions about markets, institutions, and conduct have distorted the antitrust decision-making process and produced an excessive risk of false negatives in exclusionary conduct cases involving firms attempting to achieve, maintain, or enhance dominance or substantial market power. To redress this imbalance, we integrate modern economic analysis and the teaching of decision theory with the foundational conventions of antitrust law, which has long relied on probability, presumptions, and reasonable inferences to provide more effective means for evaluating competitive effects and resolving antitrust claims.

A Multiple Rényi Entropy Based Intrusion Detection System for Connected Vehicles.

In this paper, we propose an intrusion detection system based on the estimation of the Rényi entropy with multiple orders. The Rényi entropy is a generalized notion of entropy that includes the Shannon entropy and the min-entropy as special cases. In 2018, Kim proposed an efficient estimation method for the Rényi entropy with an arbitrary real order . In this work, we utilize this method to construct a multiple order, Rényi entropy based intrusion detection system (IDS) for vehicular systems with various network connections. The proposed method estimates the Rényi entropies simultaneously with three distinct orders, two, three, and four, based on the controller area network (CAN)-IDs of consecutively generated frames. The collected frames are split into blocks with a fixed number of frames, and the entropies are evaluated based on these blocks. For a more accurate estimation against each type of attack, we also propose a retrospective sliding window method for decision of attacks based on the estimated entropies. For fair comparison, we utilized the CAN-ID attack data set generated by a research team from Korea University. Our results show that the proposed method can show the false negative and positive errors of less than 1% simultaneously.

An intelligent and blind image watermarking scheme based on hybrid SVD transforms using human visual system characteristics

This paper presents a new intelligent image watermarking scheme based on discrete wavelet transform (DWT) and singular values decomposition (SVD) using human visual system (HVS) and particle swarm optimization (PSO). The cover image is transformed by one-level (DWT) and subsequently the LL sub-band of (DWT) transformed image is chosen for embedding. To achieve the highest possible visual quality, the embedding regions are selected based on (HVS). After applying (SVD) on the selected regions, every two watermark bits are embedded indirectly into the U and $$V^{t}$$ components of SVD decomposition of the selected regions, instead of embedding one watermark bit into the U component and compensating on the $$V^{t}$$ component that results in twice capacity and reasonable imperceptibility. In addition, for increasing the robustness without losing the transparency, the scaling factors are chosen automatically by (PSO) based on the attacks test results and predefined conditions, instead of using fixed or manually set scaling factors for all different cover images. Experimental and comparative results demonstrated the stability and improved performance of the proposed scheme compared to its parents watermarking schemes. Moreover, the proposed scheme is free of false positive detection error.

Towards Detecting Building Facades with Graffiti Artwork Based on Street View Images

As a recognized type of art, graffiti is a cultural asset and an important aspect of a city’s aesthetics. As such, graffiti is associated with social and commercial vibrancy and is known to attract tourists. However, positional uncertainty and incompleteness are current issues of open geo-datasets containing graffiti data. In this paper, we present an approach towards detecting building facades with graffiti artwork based on the automatic interpretation of images from Google Street View (GSV). It starts with the identification of geo-tagged photos of graffiti artwork posted on the photo sharing media Flickr. GSV images are then extracted from the surroundings of these photos and interpreted by a customized, i.e., transfer learned, convolutional neural network. The compass heading of the GSV images classified as containing graffiti artwork and the possible positions of their acquisition are considered for scoring building facades according to their potential of containing the artwork observable in the GSV images. More than 36,000 GSV images and 5000 facades from buildings represented in OpenStreetMap were processed and evaluated. Precision and recall rates were computed for different facade score thresholds. False-positive errors are caused mostly by advertisements and scribblings on the building facades as well as by movable objects containing graffiti artwork and obstructing the facades. However, considering higher scores as threshold for detecting facades containing graffiti leads to the perfect precision rate. Our approach can be applied for identifying previously unmapped graffiti artwork and for assisting map contributors interested in the topic. Furthermore, researchers interested on the spatial correlations between graffiti artwork and socio-economic factors can profit from our open-access code and results.

Machine-learning models, cost matrices, and conservation-based reduction of selected landscape classification errors

Use of statistical models developed with machine-learning algorithms is increasing in the ecological sciences, yet these disciplines have not capitalized on the ability to use cost matrices to selectively reduce classification errors that have highly detrimental consequences. Our aim was to promote such applications by demonstrating the process of using a cost matrix to decrease specific types of misclassification, explaining the importance of exploring the effectiveness of cost matrices for a given dataset, and encouraging use of cost matrices with machine-learning models in landscape-ecological and conservation contexts. Bird occurrence data, landscape and regional land-cover data, costs of false-positive and false-negative errors, and the C5.0 decision tree algorithm were used to train and test a binary classifier. Increasing the cost for false negatives tended to decrease the frequency of this error type while allowing for reasonable predictive performance for each class separately and both classes combined. Cost matrices are applicable to many different categorical response variables and spatial scales. We encourage landscape ecologists and planners to explore the effectiveness of cost matrices for their particular dataset and project goals, especially when conservation of biodiversity across broad spatial extents is at stake.

An Adaptive Watermark Detection Algorithm for Vector Geographic Data

With the rapid development of computer and communication techniques, copyright protection of vector geographic data has attracted considerable research attention because of the high cost of such data. A novel adaptive watermark detection algorithm is proposed for vector geographic data that can be used to qualitatively analyze the robustness of watermarks against data addition attacks. First, a watermark was embedded into the vertex coordinates based on coordinate mapping and quantization. Second, the adaptive watermark detection model, which is capable of calculating the detection threshold, false positive error (FPE) and false negative error (FNE), was established, and the characteristics of the adaptive watermark detection algorithm were analyzed. Finally, experiments were conducted on several real-world vector maps to show the usability and robustness of the proposed algorithm.

Two-Stage Ransomware Detection Using Dynamic Analysis and Machine Learning Techniques

Detecting ransomware is harder than general malware because of the ever-increasing number of ransomwares with different signatures, which makes traditional signature-based detection technique powerless against ransomware. Current ransomware detection techniques usually build a complex model that incorporates various behavioral traits. The traits include suspicious file activities, API call pattern or frequency, registry keys, file extensions, etc. In this paper, we build a two-stage mixed ransomware detection model, Markov model and Random Forest model. First we focus on Windows API call sequence pattern and build a Markov model to capture the characteristics of ransomware. Next we build Random Forest machine learning model to the remaining data in order to control both false positive (FPR) and false negative (FNR) error rates. As a result of our two-stage mixed detection method we can achieve overall accuracy 97.3% with 4.8% FPR and 1.5% FNR.

Strategies in adjusting for multiple comparisons: A primer for pediatric surgeons

Background/PurposeIn pediatric surgery research, the issue of multiple comparisons commonly arises when there are multiple patient or experimental groups being compared two at a time on an outcome of interest. Performing multiple statistical comparisons increases the likelihood of finding a false positive result when there truly are no statistically significant group differences (falsely rejecting the null hypothesis when it is true). In order to control for the risk of false positive results, there are several statistical approaches that surgeons should consider in collaboration with a biostatistician when performing a study that is prone to the issue of false discovery related to multiple comparisons. It is becoming increasingly more common for high impact journals to require authors to carefully consider multiplicity in their studies. Therefore, the objective of this primer is to provide surgeons with a useful guide and recommendations on how to go about taking multiple comparisons into account to keep false positive results at an acceptable level. MethodsWe provide background on the issue of multiple comparisons and risk of type I error and guidance on statistical approaches (i.e. multiple comparisons procedures) that can be implemented to control the type I false positive error rate based on the statistical analysis plan. These include, but are not limited to, the Bonferroni correction, the False Discovery Rate (FDR) approach, Tukey's procedure, Scheffé's procedure, Holm's procedure, and Dunnett's procedure. ResultsWe present the results of the various approaches following one-way analysis of the variance (ANOVA) using a hypothetical surgical research example of the comparison between three experimental groups of rats on skin defect coverage for experimental spina bifida: the TRASCET group, sham control, and saline control. The ultimate decision in accounting for multiple comparisons is situation-dependent and surgeons should work with their statistical colleagues to ensure the best approach for controlling the type I error rate and interpreting the evidence when making multiple inferences and comparisons. ConclusionsThe risk of rejecting the null hypothesis increases when multiple hypotheses are tested using the same data. Surgeons should be aware of the available approaches and considerations to take into account multiplicity in the statistical plan or protocol of their clinical and basic science research studies. This strategy will improve their study design and ensure the most appropriate analysis of their data. Not adjusting for multiple comparisons can lead to misleading presentation of evidence to the surgical research community because of exaggerating treatment differences or effects. Type of studyReview article. Level of evidenceN/A

A novel fire index-based burned area change detection approach using Landsat-8 OLI data

ABSTRACT Change detection from multi-temporal remote sensing images is an effective way to identify the burned areas after forest fires. However, the complex image scenario and the similar spectral signatures in multispectral bands may lead to many false positive errors, which make it difficult to exact the burned areas accurately. In this paper, a novel-burned area change detection approach is proposed. It is designed based on a new Normalized Burn Ratio-SWIR (NBRSWIR) index and an automatic thresholding algorithm. The effectiveness of the proposed approach is validated on three Landsat-8 data sets presenting various fire disaster events worldwide. Compared to eight index-based detection methods that developed in the literature, the proposed approach has the best performance in terms of class separability (2.49, 1.74 and 2.06) and accuracy (98.93%, 98.57% and 99.51%) in detecting the burned areas. Simultaneously, it can also better suppress the complex irrelevant changes in the background.

PGMM—Pre-Trained Gaussian Mixture Model Based Convolution Neural Network for Electroencephalography Imagery Analysis

Electroencephalography (EEG) signal processing through imagery inputs using intelligent computation methods are practiced in the recent years for improving the accuracy of detecting neural disorders. Classification and analysis of the input imagery requires prior training and assisted error detection to improve the accuracy. In this article, pre-trained Gaussian mixture model (PGMM) is introduced for improving the accuracy of EEG signal imagery analysis. The proposed model relies on deep learning classifiers for analyzing the imagery using pixel based segmentation through pre-training models. The errors in classification are identified through recurrent convolution neural network training process as aided by the extracted features. Based on the pre-trained feature assessment, the false positive errors are mitigated to achieve a better accuracy (92%) under controlled classification time and high true positives.