Watch For Failing Objects: What Inappropriate Compliance Reveals About Shared Mental Models In Autonomous Cars

2021 ◽  
Vol 65 (1) ◽  
pp. 643-647
Author(s):  
Yosef S. Razin ◽  
Jack Gale ◽  
Jiaojiao Fan ◽  
Jaznae’ Smith ◽  
Karen M. Feigh
Keyword(s):  
Mental Models ◽  
Mental Model ◽  
False Positive Rate ◽  
Ground Truth ◽  
True Positive Rate ◽  
Shared Mental Models ◽  
Shared Mental Model ◽  
Autonomous Cars ◽  
Positive Rate ◽  
Dispositional Factors

This paper evaluates Banks et al.’s Human-AI Shared Mental Model theory by examining how a self-driving vehicle’s hazard assessment facilitates shared mental models. Participants were asked to affirm the vehicle’s assessment of road objects as either hazards or mistakes in real-time as behavioral and subjective measures were collected. The baseline performance of the AI was purposefully low (<50%) to examine how the human’s shared mental model might lead to inappropriate compliance. Results indicated that while the participant true positive rate was high, overall performance was reduced by the large false positive rate, indicating that participants were indeed being influenced by the Al’s faulty assessments, despite full transparency as to the ground-truth. Both performance and compliance were directly affected by frustration, mental, and even physical demands. Dispositional factors such as faith in other people’s cooperativeness and in technology companies were also significant. Thus, our findings strongly supported the theory that shared mental models play a measurable role in performance and compliance, in a complex interplay with trust.

scholarly journals Detection and Characterization of Rumble Strips in Roadway Video Logs

2020 ◽  
Vol 2020 (6) ◽  
pp. 50-1-50-8
Author(s):  
Deniz Aykac ◽  
Thomas Karnowski ◽  
Regina Ferrell ◽  
James S. Goddard
Keyword(s):  
Autonomous Navigation ◽  
False Positive Rate ◽  
Ground Truth ◽  
True Positive Rate ◽  
The United States ◽  
Image Correction ◽  
Lane Departure ◽  
Positive Rate ◽  
Potential Methods ◽  
Rumble Strip

State departments of transportation often maintain extensive “video logs” of their roadways that include signs, lane markings, as well as non-image-based information such as grade, curvature, etc. In this work we use the Roadway Information Database (RID), developed for the Second Strategic Highway Research Program, as a surrogate for a video log to design and test algorithms to detect rumble strips in the roadway images. Rumble strips are grooved patterns at the lane extremities designed to produce an audible queue to drivers who are in danger of lane departure. The RID contains 6,203,576 images of roads in six locations across the United States with extensive ground truth information and measurements, but the rumble strip measurements (length and spacing) were not recorded. We use an image correction process along with automated feature extraction and convolutional neural networks to detect rumble strip locations and measure their length and pitch. Based on independent measurements, we estimate our true positive rate to be 93% and false positive rate to be 10% with errors in length and spacing on the order of 0.09 meters RMS and 0.04 meters RMS. Our results illustrate the feasibility of this approach to add value to video logs after initial capture as well as identify potential methods for autonomous navigation.

Measuring Shared Mental Models in Unmanned Aircraft Systems

2015 ◽  
pp. 1188-1196
Author(s):  
Rosemarie Reynolds ◽  
Alex J. Mirot ◽  
Prince D. Nudze
Keyword(s):  
Mental Models ◽  
Mental Model ◽  
Unmanned Aircraft ◽  
Shared Mental Models ◽  
Unmanned Aircraft Systems ◽  
Unmanned Aircraft System ◽  
Team Coordination ◽  
Shared Mental Model ◽  
Aircraft Systems ◽  
Aircraft System

Unmanned aircraft systems (UASs) are becoming part of the aviation landscape, taking on the dirty, dangerous, or dull operations traditionally completed by military and specialized civil aircraft. These operations often require high levels of team coordination. Team coordination is facilitated when team members share a mental model of group tasks and the individual crewmember's responsibilities in the performance of these tasks. The shared mental model is therefore critical for unmanned aircraft system teams to complete their operational objectives. The ability to forge a shared mental model is complicated by the diverse and often distributed nature of unmanned aircraft system teams. Before strategies can be developed to create accurate shared mental models, researchers must effectively measure shared mental models. This chapter explores the measurement of shared mental models in UASs.

TOWARDS A THEORY FOR MULTIPARTY PROACTIVE COMMUNICATION IN AGENT TEAMS

2007 ◽  
Vol 16 (02) ◽  
pp. 271-298 ◽  
Author(s):  
KAIVAN KAMALI ◽  
XIAOCONG FAN ◽  
JOHN YEN
Keyword(s):  
Mental Models ◽  
Mental Model ◽  
Formal Semantics ◽  
Helping Behavior ◽  
Shared Mental Models ◽  
Shared Mental Model ◽  
Effective Teams ◽  
Agent Teams ◽  
Team Setting ◽  
Proactive Communication

Helping behavior in effective teams is achieved via some overlapping "shared mental models" that are developed and maintained by members of the team. In this paper, we take the perspective that multiparty "proactive" communication is critical for establishing and maintaining such a shared mental model among teammates, which is the basis for agents to offer proactive help and to achieve coherent teamwork. We first provide formal semantics for multiparty proactive performatives within a team setting. We then examine how such performatives result in updates to mental model of teammates, and how such updates can trigger helpful behaviors from other teammates. We also provide conversation policies for multiparty proactive performatives.

scholarly journals The Five-Factor Perceived Shared Mental Model Scale: A Consolidation of Items Across the Contemporary Literature

2022 ◽  
Vol 12 ◽  
Author(s):  
Jandre J. van Rensburg ◽  
Catarina M. Santos ◽  
Simon B. de Jong ◽  
Sjir Uitdewilligen
Keyword(s):  
Mental Models ◽  
Future Development ◽  
Mental Model ◽  
Contemporary Literature ◽  
Past Research ◽  
Shared Mental Models ◽  
Shared Mental Model ◽  
Model Scale ◽  
Dimensional Measure ◽  
Diverse Context

Literature on Shared Mental Models (SMMs) has been burgeoning in recent years and this has provided increasingly detailed insight and evidence into the importance of SMMs within specific contexts. However, because past research predominantly focused on SMM structure as measured by diverse, context-dependent measures, a consolidated multi-dimensional measure of perceived SMMs that can be used across diverse team contexts is currently lacking. Furthermore, different conceptualizations of the dimensionality of SMMs exist, which further impedes the comparison between studies. These key limitations might hinder future development in the SMM literature. We argue that the field of SMMs has now matured enough that it is possible to take a deductive approach and evaluate the prior studies in order to refine the key SMMs dimensions, operationalizations, and measurement. Hence, we take a three-stage approach to consolidate existing literature scale-based measures of SMMs, using four samples. Ultimately, this leads to a 20-item five-dimensional scale (i.e., equipment, execution, interaction, composition, and temporal SMMs) – the Five Factor Perceived Shared Mental Model Scale (5-PSMMS). Our scale provides scholars with a tool which enables the measurement, and comparison, of SMMs across diverse team contexts. It offers practitioners the option to more straightforwardly assess perceived SMMs in their teams, allowing the identification of challenges in their teams and facilitating the design of appropriate interventions.

Measuring shared mental models in healthcare

2018 ◽  
Vol 23 (5) ◽  
pp. 207-219 ◽  
Author(s):  
Logan M Gisick ◽  
Kristen L Webster ◽  
Joseph R Keebler ◽  
Elizabeth H Lazzara ◽  
Sarah Fouquet ◽  
...  
Keyword(s):  
Decision Making ◽  
Mental Models ◽  
Mental Model ◽  
Quantitative Methods ◽  
Clinical Decision Making ◽  
Measurement Methods ◽  
Shared Mental Models ◽  
Shared Mental Model ◽  
Qualitative And Quantitative ◽  
Model Measurement

Objective To review common qualitative and quantitative methods of measuring shared mental models appropriate for use in the healthcare setting. Background Shared mental models are the overlap of individuals’ set of knowledge and/or assumptions that act as the basis for understanding and decision making between individuals. Within healthcare, shared mental models facilitate effective teamwork and theorized to influence clinical decision making and performance. With the current rapid growth and expansion of healthcare teams, it is critical that we understand and correctly use shared mental model measurement methods assess optimal team performance. Unfortunately, agreement on the proper measurement of shared mental models within healthcare remains diffuse. Method This paper presents methods appropriate to measure shared mental models within healthcare. Results Multiple shared mental model measurement methods are discussed with regard to their utility within this setting, ease of use, and difficulties in deploying within the healthcare operational environment. For rigorous analysis of shared mental models, it is recommended that a combination of qualitative and quantitative analyses be employed. Conclusion There are multitude of shared mental model measurement methods that can be used in the healthcare domain; although there is no perfect solution for every situation. Researchers can utilize this article to determine the best approach for their needs.

scholarly journals PRATD: A Phased Remote Access Trojan Detection Method with Double-Sided Features

Electronics ◽  
2020 ◽  
Vol 9 (11) ◽  
pp. 1894
Author(s):  
Chun Guo ◽  
Zihua Song ◽  
Yuan Ping ◽  
Guowei Shen ◽  
Yuhei Cui ◽  
...  
Keyword(s):  
False Positive ◽  
Detection Method ◽  
False Positive Rate ◽  
True Positive Rate ◽  
Remote Access ◽  
Detection Methods ◽  
Security Threats ◽  
True Positive ◽  
Trojan Detection ◽  
Positive Rate

Remote Access Trojan (RAT) is one of the most terrible security threats that organizations face today. At present, two major RAT detection methods are host-based and network-based detection methods. To complement one another’s strengths, this article proposes a phased RATs detection method by combining double-side features (PRATD). In PRATD, both host-side and network-side features are combined to build detection models, which is conducive to distinguishing the RATs from benign programs because that the RATs not only generate traffic on the network but also leave traces on the host at run time. Besides, PRATD trains two different detection models for the two runtime states of RATs for improving the True Positive Rate (TPR). The experiments on the network and host records collected from five kinds of benign programs and 20 famous RATs show that PRATD can effectively detect RATs, it can achieve a TPR as high as 93.609% with a False Positive Rate (FPR) as low as 0.407% for the known RATs, a TPR 81.928% and FPR 0.185% for the unknown RATs, which suggests it is a competitive candidate for RAT detection.

Ascertaining an efficient eligibility cut-off for extended Medicare items for eating disorders

2021 ◽  
pp. 103985622110286
Author(s):  
Tracey Wade ◽  
Jamie-Lee Pennesi ◽  
Yuan Zhou
Keyword(s):  
Eating Disorders ◽  
Eating Disorder ◽  
False Positive Rate ◽  
Area Under The Curve ◽  
Rate Sensitivity ◽  
True Positive Rate ◽  
Eating Disorder Examination Questionnaire ◽  
Eating Disorder Examination ◽  
Positive Rate ◽  
The Relationship

Objective: Currently eligibility for expanded Medicare items for eating disorders (excluding anorexia nervosa) require a score ⩾ 3 on the 22-item Eating Disorder Examination-Questionnaire (EDE-Q). We compared these EDE-Q “cases” with continuous scores on a validated 7-item version of the EDE-Q (EDE-Q7) to identify an EDE-Q7 cut-off commensurate to 3 on the EDE-Q. Methods: We utilised EDE-Q scores of female university students ( N = 337) at risk of developing an eating disorder. We used a receiver operating characteristic (ROC) curve to assess the relationship between the true-positive rate (sensitivity) and the false-positive rate (1-specificity) of cases ⩾ 3. Results: The area under the curve showed outstanding discrimination of 0.94 (95% CI: .92–.97). We examined two specific cut-off points on the EDE-Q7, which included 100% and 87% of true cases, respectively. Conclusion: Given the EDE-Q cut-off for Medicare is used in conjunction with other criteria, we suggest using the more permissive EDE-Q7 cut-off (⩾2.5) to replace use of the EDE-Q cut-off (⩾3) in eligibility assessments.

scholarly journals Improving Ecological Inference by Predicting Individual Ethnicity from Voter Registration Records

2016 ◽  
Vol 24 (2) ◽  
pp. 263-272 ◽  
Author(s):  
Kosuke Imai ◽  
Kabir Khanna
Keyword(s):  
Mean Squared Error ◽  
False Positive Rate ◽  
True Positive Rate ◽  
Voter Registration ◽  
Racial Groups ◽  
Ecological Inference ◽  
Inference Problem ◽  
Individual Level ◽  
Positive Rate ◽  
Election Results

In both political behavior research and voting rights litigation, turnout and vote choice for different racial groups are often inferred using aggregate election results and racial composition. Over the past several decades, many statistical methods have been proposed to address this ecological inference problem. We propose an alternative method to reduce aggregation bias by predicting individual-level ethnicity from voter registration records. Building on the existing methodological literature, we use Bayes's rule to combine the Census Bureau's Surname List with various information from geocoded voter registration records. We evaluate the performance of the proposed methodology using approximately nine million voter registration records from Florida, where self-reported ethnicity is available. We find that it is possible to reduce the false positive rate among Black and Latino voters to 6% and 3%, respectively, while maintaining the true positive rate above 80%. Moreover, we use our predictions to estimate turnout by race and find that our estimates yields substantially less amounts of bias and root mean squared error than standard ecological inference estimates. We provide open-source software to implement the proposed methodology.

scholarly journals Mixture models reveal multiple positional bias types in RNA-Seq data and lead to accurate transcript concentration estimates

2014 ◽  
Author(s):  
Andreas Tuerk ◽  
Gregor Wiktorin ◽  
Serhat Güler
Keyword(s):  
Probability Distributions ◽  
False Positive Rate ◽  
Synthetic Data ◽  
True Positive Rate ◽  
Rna Seq ◽  
Microarray Quality Control ◽  
Data Set ◽  
Rna Transcripts ◽  
Positive Rate ◽  
Fragment Distribution

Quantification of RNA transcripts with RNA-Seq is inaccurate due to positional fragment bias, which is not represented appropriately by current statistical models of RNA-Seq data. This article introduces the Mix2(rd. "mixquare") model, which uses a mixture of probability distributions to model the transcript specific positional fragment bias. The parameters of the Mix2model can be efficiently trained with the Expectation Maximization (EM) algorithm resulting in simultaneous estimates of the transcript abundances and transcript specific positional biases. Experiments are conducted on synthetic data and the Universal Human Reference (UHR) and Brain (HBR) sample from the Microarray quality control (MAQC) data set. Comparing the correlation between qPCR and FPKM values to state-of-the-art methods Cufflinks and PennSeq we obtain an increase in R2value from 0.44 to 0.6 and from 0.34 to 0.54. In the detection of differential expression between UHR and HBR the true positive rate increases from 0.44 to 0.71 at a false positive rate of 0.1. Finally, the Mix2model is used to investigate biases present in the MAQC data. This reveals 5 dominant biases which deviate from the common assumption of a uniform fragment distribution. The Mix2software is available at http://www.lexogen.com/fileadmin/uploads/bioinfo/mix2model.tgz.

scholarly journals Exploring Author Profiling for Fake News Detection

2021 ◽  
Author(s):  
Shloak Rathod
Keyword(s):  
False Positive Rate ◽  
True Positive Rate ◽  
Supervised Machine Learning ◽  
Online Media ◽  
Fake News ◽  
Writing Style ◽  
Short Term ◽  
Positive Rate ◽  
Rapid Dissemination ◽  
Author Profiling

<div><div><div><p>The proliferation of online media allows for the rapid dissemination of unmoderated news, unfortunately including fake news. The extensive spread of fake news poses a potent threat to both individuals and society. This paper focuses on designing author profiles to detect authors who are primarily engaged in publishing fake news articles. We build on the hypothesis that authors who write fake news repeatedly write only fake news articles, at least in short-term periods. Fake news authors have a distinct writing style compared to real news authors, who naturally want to maintain trustworthiness. We explore the potential to detect fake news authors by designing authors’ profiles based on writing style, sentiment, and co-authorship patterns. We evaluate our approach using a publicly available dataset with over 5000 authors and 20000 articles. For our evaluation, we build and compare different classes of supervised machine learning models. We find that the K-NN model performed the best, and it could detect authors who are prone to writing fake news with an 83% true positive rate with only a 5% false positive rate.</p></div></div></div>

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