When and why threats go undetected: Impacts of event rate and shift length on threat detection accuracy during airport baggage screening

Objective: We aimed to assess the impact of task demands and individual characteristics on threat detection in baggage screeners. Background: Airport security staff work under time constraints to ensure optimal threat detection. Understanding the impact of individual characteristics and task demands on performance is vital to ensure accurate threat detection. Method: We examined threat detection in baggage screeners as a function of event rate (i.e., number of bags per minute) and time on task across 4 months. We measured performance in terms of the accuracy of detection of Fictitious Threat Items (FTIs) randomly superimposed on X-ray images of real passenger bags. Results: Analyses of the percentage of correct FTI identifications (hits) show that longer shifts with high baggage throughput result in worse threat detection. Importantly, these significant performance decrements emerge within the first 10 min of these busy screening shifts only. Conclusion: Longer shift lengths, especially when combined with high baggage throughput, increase the likelihood that threats go undetected. Application: Shorter shift rotations, although perhaps difficult to implement during busy screening periods, would ensure more consistently high vigilance in baggage screeners and, therefore, optimal threat detection and passenger safety.

The SAFEE on-board threat detection system

Under the framework of the European Union Funded SAFEE project(1), this paper gives an overview of a novel monitoring and scene analysis system developed for use onboard aircraft in spatially constrained environments. The techniques discussed herein aim to warn on-board crew about pre-determined indicators of threat intent (such as running or shouting in the cabin), as elicited from industry and security experts. The subject matter experts believe that activities such as these are strong indicators of the beginnings of undesirable chains of events or scenarios, which should not be allowed to develop aboard aircraft. This project aimes to detect these scenarios and provide advice to the crew. These events may involve unruly passengers or be indicative of the precursors to terrorist threats. With a state of the art tracking system using homography intersections of motion images, and probability based Petri nets for scene understanding, the SAFEE behavioural analysis system automatically assesses the output from multiple intelligent sensors, and creates. recommendations that are presented to the crew using an integrated airborn user interface. Evaluation of the system is conducted within a full size aircraft mockup, and experimental results are presented, showing that the SAFEE system is well suited to monitoring people in confined environments, and that meaningful and instructive output regarding human actions can be derived from the sensor network within the cabin.

Instantaneous threat detection based on a semantic representation of activities, zones and trajectories

Threat detection is a challenging problem, because threats appear in many variations and differences to normal behaviour can be very subtle. In this paper, we consider threats on a parking lot, where theft of a truck’s cargo occurs. The threats range from explicit, e.g. a person attacking the truck driver, to implicit, e.g. somebody loitering and then fiddling with the exterior of the truck in order to open it. Our goal is a system that is able to recognize a threat instantaneously as they develop. Typical observables of the threats are a person’s activity, presence in a particular zone and the trajectory. The novelty of this paper is an encoding of these threat observables in a semantic, intermediate-level representation, based on low-level visual features that have no intrinsic semantic meaning themselves. The aim of this representation was to bridge the semantic gap between the low-level tracks and motion and the higher-level notion of threats. In our experiments, we demonstrate that our semantic representation is more descriptive for threat detection than directly using low-level features. We find that a person’s activities are the most important elements of this semantic representation, followed by the person’s trajectory. The proposed threat detection system is very accurate: 96.6 % of the tracks are correctly interpreted, when considering the temporal context.

System of systems approach to threat detection and integration of heterogeneous independently operable systems

This paper presents a system of systems approach to threat detection through integration of heterogeneous independently operable systems. The approach is presented on a realistic situation where a human-controlled base robot, swarm robot(s), and sensors work together to obtain a decision about a possible threat in the environment. The base robot is remotely operated by a human using a haptic control system. The swarm robot(s) are autonomous and can accept directives from the base robot. Finally, sensors directly communicate with (report to) the base robot. In this scenario, heterogeneous systems and human interact in a system of systems architecture. With the inclusion of human expert and sensor verification of swarm robots, the system can successfully perform the threat detection and reduce the false alarms. Finally, a system of systems simulation framework including a base robot, a swarm robot, and two sensors is presented in addition to an experimental evaluation of the proposed SoS architecture

Data breach - services for threat detection, analysis and response

Nel mondo della sicurezza informatica, le tecnologie si evolvono per far fronte alle minacce. Non è possibile prescindere dalla prevenzione, ma occorre accettare il fatto che nessuna barriera risulterà impenetrabile e che la rilevazione, unitamente ad una pronta risposta, rappresenta una linea estremamente critica di difesa, ma l’unica veramente attuabile per poter guadagnare più tempo possibile o per limitare i danni. Introdurremo quindi un nuovo modello operativo composto da procedure capaci di affrontare le nuove sfide che il malware costantemente offre e allo stesso tempo di sollevare i comparti IT da attività onerose e sempre più complesse, ottimizzandone il processo di comunicazione e di risposta.

Supernatural Beliefs, Unconscious Threat and Judgment Bias in Tibetan Buddhists

Individuals who have been subtly reminded of death display heightened in-group favouritism, or “worldview defense.” Terror management theory argues (i) that death cues engender worldview defense via psychological mechanisms specifically evolved to suppress death anxiety, and (ii) that the core function of religiosity is to suppress death anxiety. Thus, terror management theory predicts that extremely religious individuals will not evince worldview defense. Here, two studies are presented in support of an alternative perspective. According to the unconscious vigilance hypothesis, subtly processed threats (which need not pertain to death) heighten sensitivity to affectively valenced stimuli (which need not pertain to cultural attitudes). From this perspective, religiosity mitigates the influence of mortality-salience only insofar as afterlife doctrines reduce the perceived threat posed by death. Tibetan Buddhism portrays death as a perilous gateway to rebirth rather than an end to suffering; faith in this doctrine should therefore not be expected to nullify mortality-salience effects. In Study 1, devout Tibetan Buddhists who were subtly reminded of death produced exaggerated aesthetic ratings unrelated to cultural worldviews. In Study 2, devout Tibetan Buddhists produced worldview defense following subliminal exposure to non-death cues of threat. The results demonstrate both the domain-generality of the process underlying worldview defense and the importance of religious doctrinal content in moderating mortality-salience effects.

Robust abandoned object detection integrating wide area visual surveillance and social context

This paper presents a video surveillance framework that robustly and efficiently detects abandoned objects in surveillance scenes. The framework is based on a novel threat assessment algorithm which combines the concept of ownership with automatic understanding of social relations in order to infer abandonment of objects. Implementation is achieved through development of a logic-based inference engine based on Prolog. Threat detection performance is conducted by testing against a range of datasets describing realistic situations and demonstrates a reduction in the number of false alarms generated. The proposed system represents the approach employed in the EU SUBITO project (Surveillance of Unattended Baggage and the Identification and Tracking of the Owner).

Airborne collision scenario flight tests : impact of angle measurement errors on reactive vision-based avoidance control

The future emergence of many types of airborne vehicles and unpiloted aircraft in the national airspace means collision avoidance is of primary concern in an uncooperative airspace environment. The ability to replicate a pilot’s see and avoid capability using cameras coupled with vision based avoidance control is an important part of an overall collision avoidance strategy. But unfortunately without range collision avoidance has no direct way to guarantee a level of safety. Collision scenario flight tests with two aircraft and a monocular camera threat detection and tracking system were used to study the accuracy of image-derived angle measurements. The effect of image-derived angle errors on reactive vision-based avoidance performance was then studied by simulation. The results show that whilst large angle measurement errors can significantly affect minimum ranging characteristics across a variety of initial conditions and closing speeds, the minimum range is always bounded and a collision never occurs.

Amorphous silicon thin film transistor biosensing system

Electronic systems are a very good platform for sensing biological signals for fast point-of-care diagnostics or threat detection. One of the solutions is the lab-on-a-chip integrated circuit (IC), which is low cost and high reliability, offering the possibility for label-free detection. In recent years, similar integrated biosensors based on the conventional complementary metal oxide semiconductor (CMOS) technology have been reported. However, post-fabrication processes are essential for all classes of CMOS biochips, requiring biocompatible electrode deposition and circuit encapsulation. In this work, we present an amorphous silicon (a-Si) thin film transistor (TFT) array based sensing approach, which greatly simplifies the fabrication procedures and even decreases the cost of the biosensor. The device contains several identical sensor pixels with amplifiers to boost the sensitivity. Ring oscillator and logic circuits are also integrated to achieve different measurement methodologies, including electro-analytical methods such as amperometric and cyclic voltammetric modes. The system also supports different operational modes. For example, depending on the required detection arrangement, a sample droplet could be placed on the sensing pads or the device could be immersed into the sample solution for real time in-situ measurement. The entire system is designed and fabricated using a low temperature TFT process that is compatible to plastic substrates. No additional processing is required prior to biological measurement. A Cr/Au double layer is used for the biological-electronic interface. The success of the TFT-based system used in this work will open new avenues for flexible label-free or low-cost disposable biosensors. © 2013 Materials Research Society.

Towards optimization of a real-world robotic-sensor system of systems

The problem of threat detection in an unstructured environment is considered. Three systems, comprising of robots and sensors, are proposed to form a system of systems (SoS) to find a solution to the problem. System interactions are defined to provide a framework for formulation as an SoS optimization problem. Different cost and objective functions are introduced for optimization of local criteria. Using different weights, a linear combination of the local cost and objective functions is obtained to propose a global objective function. An algorithm is suggested to find an optimum value for the global objective function leading towards optimization of the SoS.

Efficient Deployment of Mobile Detectors for Security Applications

We present a real-world problem that arises in security threat detection applications. The problem consists of deploying mobile detectors on moving units that follow predefined routes. Examples of such units are buses, coaches, and trolleys. Due to a limited budget not all available units can be equipped with a detector. The goal is to equip a subset of units such that the utility of the resulting coverage is maximized. Existing methods for detector deployment are designed to place detectors in fixed locations and are therefore not applicable to the problem considered here. We formulate the planning problem as a binary linear program and present a coverage heuristic for generating effective deployments in short CPU time. The heuristic has theoretical performance guarantees for important special cases of the problem. The effectiveness of the coverage heuristic is demonstrated in a computational analysis based on 28 instances that we derived from real-world data.

Localized surface plasmon resonance biosensors for real-time biomolecular binding study

Surface Plasmon Resonance (SPR) and localized surface plasmon resonance (LSPR) biosensors have brought a revolutionary change to in vitro study of biological and biochemical processes due to its ability to measure extremely small changes in surface refractive index (RI), binding equilibrium and kinetics. Strategies based on LSPR have been employed to enhance the sensitivity for a variety of applications, such as diagnosis of diseases, environmental analysis, food safety, and chemical threat detection. In LSPR spectroscopy, absorption and scattering of light are greatly enhanced at frequencies that excite the LSPR, resulting in a characteristic extinction spectrum that depends on the RI of the surrounding medium. Compositional and conformational change within the surrounding medium near the sensing surface could therefore be detected as shifts in the extinction spectrum. This dissertation specifically focuses on the development and evaluation of highly sensitive LSPR biosensors for in situ study of biomolecular binding process by incorporating nanotechnology. Compared to traditional methods for biomolecular binding studies, LSPR-based biosensors offer real-time, label free detection. First, we modified the gold sensing surface of LSPR-based biosensors using nanomaterials such as gold nanoparticles (AuNPs) and polymer to enhance surface absorption and sensitivity. The performance of this type of biosensors was evaluated on the application of small heavy metal molecule binding affinity study. This biosensor exhibited ∼7 fold sensitivity enhancement and binding kinetics measurement capability comparing to traditional biosensors. Second, a miniaturized cell culture system was integrated into the LSPR-based biosensor system for the purpose of real-time biomarker signaling pathway studies and drug efficacy studies with living cells. To the best of our knowledge, this is the first LSPR-based sensing platform with the capability of living cell studies. We demonstrated the living cell measurement ability by studying the VEGF signaling pathway in living SKOV-3 cells. Results have shown that the VEGF secretion level from SKOV-3 cells is 0.0137 ± 0.0012 pg per cell. Moreover, we have demonstrated bevacizumab drug regulation to the VEGF signaling pathway using this biosensor. This sensing platform could potentially help studying biomolecular binding kinetics which elucidates the underlying mechanisms of biotransportation and drug delivery.

Exploring factors related to the anger superiority effect in children with Autism Spectrum Disorder

Despite face and emotion recognition deficits, individuals with Autism Spectrum Disorder (ASD) appear to experience the anger superiority effect, where an angry face in a crowd is detected faster than a neutral face. This study extended past research to examine the impacts of ecologically valid photographic stimuli, gender and anxiety symptoms on the anger superiority effect in children with and without ASD. Participants were 81, 7-12year old children, 42 with ASD matched on age, gender and perceptual IQ to 39 typically developing (TYP) children. The photographic stimuli did not impact on task performance in ASD with both groups exhibiting the anger superiority effect. There were no gender differences and no associations with anxiety. Age was associated with the effect in the TYP but not ASD group. These findings confirm a robust effect of speeded detection of threat in ASD which does not appear to be confounded by gender or anxiety, but may have different underlying age-associated mechanisms.

Localized Surface Plasmon Resonance Biosensors for Real-Time Biomolecular Binding Study

Surface Plasmon Resonance (SPR) and localized surface plasmon resonance (LSPR) biosensors have brought a revolutionary change to in vitro study of biological and biochemical processes due to its ability to measure extremely small changes in surface refractive index (RI), binding equilibrium and kinetics. Strategies based on LSPR have been employed to enhance the sensitivity for a variety of applications, such as diagnosis of diseases, environmental analysis, food safety, and chemical threat detection. In LSPR spectroscopy, absorption and scattering of light are greatly enhanced at frequencies that excite the LSPR, resulting in a characteristic extinction spectrum that depends on the RI of the surrounding medium. Compositional and conformational change within the surrounding medium near the sensing surface could therefore be detected as shifts in the extinction spectrum. This dissertation specifically focuses on the development and evaluation of highly sensitive LSPR biosensors for in situ study of biomolecular binding process by incorporating nanotechnology. Compared to traditional methods for biomolecular binding studies, LSPR-based biosensors offer real-time, label free detection. First, we modified the gold sensing surface of LSPR-based biosensors using nanomaterials such as gold nanoparticles (AuNPs) and polymer to enhance surface absorption and sensitivity. The performance of this type of biosensors was evaluated on the application of small heavy metal molecule binding affinity study. This biosensor exhibited ~7 fold sensitivity enhancement and binding kinetics measurement capability comparing to traditional biosensors. Second, a miniaturized cell culture system was integrated into the LSPR-based biosensor system for the purpose of real-time biomarker signaling pathway studies and drug efficacy studies with living cells. To the best of our knowledge, this is the first LSPR-based sensing platform with the capability of living cell studies. We demonstrated the living cell measurement ability by studying the VEGF signaling pathway in living SKOV-3 cells. Results have shown that the VEGF secretion level from SKOV-3 cells is 0.0137 ± 0.0012 pg per cell. Moreover, we have demonstrated bevacizumab drug regulation to the VEGF signaling pathway using this biosensor. This sensing platform could potentially help studying biomolecular binding kinetics which elucidates the underlying mechanisms of biotransportation and drug delivery.