Mitigating the Effects of Task Switching in Baggage Screeners

Using a simulated baggage screening task, we investigated two literature-supported mitigation strategies for reducing the negative effects of task switching, namely less frequent switching and memory support. The study replicates widely reported switching effects on a complex task. The results also show that people can improve performance when provided memory support. When task switching, people can struggle to retrieve the correct task instruction due to the automatic process behind functional memory decay. Memory support reduces the negative effects of functional decay by providing people a reminder.

A multi-level weighted alarm security system for passenger and checked-baggage screening

Persistent and ever-changing threat of terrorism has led to the evolution of security systems in the air transportation industry. Passenger and checked-baggage screening prior to boarding an aircraft has become a priority for the airport security system. We propose a two-stage screening system by integrating the passenger prescreening and a multi-level checked-baggage screening. Based on the concept of the weighted k-out-of-n system, we introduce an integrated weighted alarm security screening system for a multi-level baggage screening system, where the system alarms when the total weight of its working levels is greater than a predefined system alarm threshold. To improve the security and efficiency of aviation systems, two optimization models are formulated to enhance the detection of possible threats in the passenger and baggage screening with consideration of the system life cycle cost. The optimal solutions of the threshold values are obtained for screening technologies and passenger classes to achieve the maximum security and efficiency. Numerical analysis is implemented to demonstrate the effectiveness of the proposed two-stage screening system.

How Feature Context alters Attentional Template Switching

In real-world tasks visual attention is rarely aimed at a single object. Humans rather“forage” the visual scene for information, dynamically switching attentional templates. Several visual search studies have found that observers often use suboptimal attentional control strategies, possibly to avoid effort. Here, we investigated with a foraging paradigm if observers’ reluctance to switch between attentional templates increases with template specificity. To that end, we manipulated the feature context of displays in which participants “foraged” moving stimuli on a tablet-PC. Experiment 1 (N = 35) revealed a decline in switching tendency and foraging efficiency with increasing feature-space distance between target alternatives. Experiment 2 (N = 36) found even lower flexibility with distractor color close to, and strongest impairments with distractor color in between target colors. Our results demonstrate that visualinformation sampling is most flexible when broad (instead of very specific) templates and relational search strategies are possible (e.g., attending “redder” objects), with implications for both attention research and applications, especially in visual-foraging-like tasks, such as baggage screening or medical image assessment.

DEBISim: A simulation pipeline for dual energy CT-based baggage inspection systems1

BACKGROUND: Materials characterization made possible by dual energy CT (DECT) scanners is expected to considerably improve automatic detection of hazardous objects in checked and carry-on luggage at our airports. Training a computer to identify the hazardous items from DECT scans however implies training on a baggage dataset that can represent all the possible ways a threat item can packed inside a bag. Practically, however, generating such data is made challenging by the logistics (and the permissions) related to the handling of the hazardous materials. OBJECTIVE: The objective of this study is to present a software simulation pipeline that eliminates the need for a human to handle dangerous materials and that allows for virtually unlimited variability in the placement of such materials in a bag alongside benign materials. METHODS: In this paper, we present our DEBISim software pipeline that carries out an end-to-end simulation of a DECT scanner for virtual bags. The key highlights of DEBISim are: (i) A 3D user-interactive graphics editor for constructing a virtual 3D bag with manual placement of different types of objects in it; (ii) An automated virtual bag generation algorithm for creating randomized baggage datasets; (iii) An ability to spawn deformable sheets and liquid-filled containers in a virtual bag to represent plasticized and liquid explosives; and (iv) A GPU-based X-ray forward modelling block for spiral cone-beam scanners used in checked baggage screening. RESULTS: We have tested our simulator using two standard CT phantoms: the American College of Radiology (ACR) phantom and the NIST security screening phantom as well as on a set of reference materials representing commonly encountered items in checked baggage. For these phantoms, we have assessed the quality of the simulator by comparing the simulated data reconstructions with real CT scans of the same phantoms. The comparison shows that the material-specific properties as well as the CT artifacts in the scans generated by DEBISim are close to those produced by an actual scanner. CONCLUSION: DEBISim is an end-to-end simulation framework for rapidly generating X-ray baggage data for dual energy cone-beam scanners.

Measuring the Efficiency of Automation-Aided Performance in a Simulated Baggage Screening Task

Objective The present study replicated and extended prior findings of suboptimal automation use in a signal detection task, benchmarking automation-aided performance to the predictions of several statistical models of collaborative decision making. Background Though automated decision aids can assist human operators to perform complex tasks, operators often use the aids suboptimally, achieving performance lower than statistically ideal. Method Participants performed a simulated security screening task requiring them to judge whether a target (a knife) was present or absent in a series of colored X-ray images of passenger baggage. They completed the task both with and without assistance from a 93%-reliable automated decision aid that provided a binary text diagnosis. A series of three experiments varied task characteristics including the timing of the aid’s judgment relative to the raw stimuli, target certainty, and target prevalence. Results and Conclusion Automation-aided performance fell closest to the predictions of the most suboptimal model under consideration, one which assumes the participant defers to the aid’s diagnosis with a probability of 50%. Performance was similar across experiments. Application Results suggest that human operators’ performance when undertaking a naturalistic search task falls far short of optimal and far lower than prior findings using an abstract signal detection task.