Biblio

With novel 3D imaging technology based on computed tomography (CT) set to replace the current 2D X-ray systems, airports face the challenge of adequately preparing airport security officers (screeners) through knowledge building. Virtual reality (VR) bears the potential to greatly facilitate this process by allowing learners to experience and engage in immersive virtual scenarios as if they were real. However, while general aspects of immersion have been explored frequently, less is known about the benefits of immersive technology for instructional purposes in practical settings such as airport security.In the present study, we evaluated how different display technologies (2D vs VR) and segmentation (system-paced vs learner-paced) affected screeners' objective and subjective knowledge gain, cognitive load, as well as aspects of motivation and technology acceptance. By employing a 2 x 2 between-subjects design, four experimental groups experienced uniform learning material featuring information about 3D CT technology and its application in airport security: 2D system-paced, 2D learner-paced, VR system-paced, and VR learner-paced. The instructional material was presented as an 11 min multimedia lesson featuring words (i.e., narration, onscreen text) and pictures in dynamic form (i.e., video, animation). Participants of the learner-paced groups were prompted to initialize the next section of the multimedia lesson by pressing a virtual button after short segments of information. Additionally, a control group experiencing no instructional content was included to evaluate the effectiveness of the instructional material. The data was collected at an international airport with screeners having no prior 3D CT experience (n=162).The results show main effects on segmentation for objective learning outcomes (favoring system-paced), germane cognitive load on display technology (supporting 2D). These results contradict the expected benefits of VR and segmentation, respectively. Overall, the present study offers valuable insight on how to implement instructional material for a practical setting.

Due to the noise in the images, the edges extracted from these noisy images are always discontinuous and inaccurate by traditional operators. In order to solve these problems, this paper proposes multi-direction edge detection operator to detect edges from noisy images. The new operator is designed by introducing the shear transformation into the traditional operator. On the one hand, the shear transformation can provide a more favorable treatment for directions, which can make the new operator detect edges in different directions and overcome the directional limitation in the traditional operator. On the other hand, all the single pixel edge images in different directions can be fused. In this case, the edge information can complement each other. The experimental results indicate that the new operator is superior to the traditional ones in terms of the effectiveness of edge detection and the ability of noise rejection.

Certificate Transparency (CT) is an emerging system for enabling the rapid discovery of malicious or misissued certificates. Initially standardized in 2013, CT is now finally beginning to see widespread support. Although CT provides desirable security benefits, web browsers cannot begin requiring all websites to support CT at once, due to the risk of breaking large numbers of websites. We discuss challenges for deployment, analyze the adoption of CT on the web, and measure the error rates experienced by users of the Google Chrome web browser. We find that CT has so far been widely adopted with minimal breakage and warnings. Security researchers often struggle with the tradeoff between security and user frustration: rolling out new security requirements often causes breakage. We view CT as a case study for deploying ecosystem-wide change while trying to minimize end user impact. We discuss the design properties of CT that made its success possible, as well as draw lessons from its risks and pitfalls that could be avoided in future large-scale security deployments.

The security of web communication via the SSL/TLS protocols relies on safe distributions of public keys associated with web domains in the form of X.509 certificates. Certificate authorities (CAs) are trusted third parties that issue these certificates. However, the CA ecosystem is fragile and prone to compromises. Starting with Google's Certificate Transparency project, a number of research works have recently looked at adding transparency for better CA accountability, effectively through public logs of all certificates issued by certification authorities, to augment the current X.509 certificate validation process into SSL/TLS. In this paper, leveraging recent progress in blockchain technology, we propose a novel system, called CTB, that makes it impossible for a CA to issue a certificate for a domain without obtaining consent from the domain owner. We further make progress to equip CTB with certificate revocation mechanism. We implement CTB using IBM's Hyperledger Fabric blockchain platform. CTB's smart contract, written in Go, is provided for complete reference.

Dual Energy CT (DECT) has recently gained significant research interest owing to its ability to discriminate materials, and hence is widely applied in the field of nuclear safety and security inspection. With the current technological developments, DECT can be typically realized by using two sets of detectors, one for detecting lower energy X-rays and another for detecting higher energy X-rays. This makes the imaging system expensive, limiting its practical implementation. In 2009, our group performed a preliminary study on a new low-cost system design, using only a complete data set for lower energy level and a sparse data set for the higher energy level. This could significantly reduce the cost of the system, as it contained much smaller number of detector elements. Reconstruction method is the key point of this system. In the present study, we further validated this system and proposed a robust method, involving three main steps: (1) estimation of the missing data iteratively with TV constraints; (2) use the reconstruction from the complete lower energy CT data set to form an initial estimation of the projection data for higher energy level; (3) use ordered views to accelerate the computation. Numerical simulations with different number of detector elements have also been examined. The results obtained in this study demonstrate that 1 + 14% CT data is sufficient enough to provide a rather good reconstruction of both the effective atomic number and electron density distributions of the scanned object, instead of 2 sets CT data.