A Step toward Ending Long Airport Security Lines: The Modified Boarding Pass

Anyone who has traveled by air has most likely experienced long airport security lines. Yet not much is known about its cause because few have considered if passengers have created this problem for themselves. The present study attempts to fill this research gap by suggesting that when passengers are not well-prepared for security screening, they delay the process by making mistakes and not complying with procedures. This lack of preparedness can be attributed to several shortcomings of security signposts. This study proposes the use of a modified boarding pass as an alternative form of signage to help passengers better prepare for security screening. In a recall evaluation of the items to remove prior to security screening, the combination of the modified boarding pass and security signposts led to greater recall than when either stimuli were used alone. In an airport survey to gather public sentiment, three-quarters of the respondents saw value in the idea of the modified boarding pass. Although the majority of the respondents were receptive to it becoming an option for future travel, many also felt that the modified boarding pass would be more useful than security signposts or announcements at conveying helpful security screening information

DYNAMIC BIOMETRIC SIGNATURE AS AN EFFICIENT TOOL FOR INTERNAL CORPORATE COMMUNICATION

Cílem práce je podat ucelenou informaci o možnostech autentizace, kombinace autentizačních faktorů a začlenění této problematiky do podnikové komunikace. Práce se zaměřuje na tuto problematiku a specifikuje možnosti získání autentizačních informací, dále analyzuje metody autentizace, identifikace a autorizace. Zkoumaná je využitelnost biometrických technologií, princip funkčnosti, příklady jejich použití, jejich vliv, výhody a nevýhody, které přinášejí. Přirozený, snadno dostupný, a tedy vhodný nástroj pro efektivní a bezpečnou komunikaci je autentizace zahrnující dynamický biometrický podpis. Problematika technologie dynamického biometrického podpisu a jeho implementace jsou zkoumány z komplexního hlediska včetně provedených experimentů. Z výzkumu vyplývá, že dynamický biometrický podpis dokáže sloužit jako metoda podporující bezpečnou podnikovou komunikaci a zredukovat autentizační rizika ve společnostech i pro jednotlivce.The aim of this thesis is to provide comprehensive information on the possibilities of authentication, combination of authentication factors and the integration of this issue into corporate communication. The work focuses on this issue and specifies the possibilities for obtaining authentication information, analyses the authentication methods, identification and authorization. It examines the applicability of biometric technologies, the principle of their functionality, examples of their use, their impact, the advantages and disadvantages they bring. A natural, easy-to-use, convenient tool for effective and secure communication is authentication including the dynamic biometric signature. The issues of the dynamic biometric signature technology and its implementation are examined from a comprehensive perspective involving experiments. The research proved that the dynamic biometric signature can serve as a method for supporting secure corporate communication and reduce authentication risks in companies and for individuals.

Industry 4.0 : challenges and success factors for adopting digital technologies in airports

With the advent of Industry 4.0 technologies in the last decade, airports have undergone digitalisation to capitalise on the purported benefits of these technologies such as improved operational efficiency and passenger experience. The ongoing COVID-19 pandemic with emergence of its variants (e.g. Delta, Omicron) has exacerbated the need for airports to adopt new technologies such as contactless and robotic technologies to facilitate travel during this pandemic. However, there is limited knowledge of recent challenges and success factors for adoption of digital technologies in airports. Therefore, through an industry survey of airport operators and managers around the world (n=102, 0.75

Visual Saliency Estimation Via HEVC Bitstream Analysis

Abstract Since Information Technology developed dramatically from the last century 50's, digital images and video are ubiquitous. In the last decade, image and video processing have become more and more popular in biomedical, industrial, art and other fields. People made progress in the visual information such as images or video display, storage and transmission. The attendant problem is that video processing tasks in time domain become particularly arduous. Based on the study of the existing compressed domain video saliency detection model, a new saliency estimation model for video based on High Efficiency Video Coding (HEVC) is presented. First, the relative features are extracted from HEVC encoded bitstream. The naive Bayesian model is used to train and test features based on original YUV videos and ground truth. The intra frame saliency map can be achieved after training and testing intra features. And inter frame saliency can be achieved by intra saliency with moving motion vectors. The ROC of our proposed intra mode is 0.9561. Other classification methods such as support vector machine (SVM), k nearest neighbors (KNN) and the decision tree are presented to compare the experimental outcomes. The variety of compression ratio has been analysis to affect the saliency

Human factors in X-ray image inspection of passenger Baggage – Basic and applied perspectives

The X-ray image inspection of passenger baggage contributes substantially to aviation security and is best understood as a search and decision task: Trained security officers – so called screeners – search the images for threats among many harmless everyday objects, but the recognition of objects in X-ray images and therefore the decision between threats and harmless objects can be difficult. Because performance in this task depends on often difficult recognition, it is not clear to what extent basic research on visual search can be generalized to X-ray image inspection. Manuscript 1 of this thesis investigated whether X-ray image inspection and a traditional visual search task depend on the same visual-cognitive abilities. The results indicate that traditional visual search tasks and X-ray image inspection depend on different aspects of common visual-cognitive abilities. Another gap between basic research on visual search and applied research on X-ray image inspection is that the former is typically conducted with students and the latter with professional screeners. Therefore, these two populations were compared, revealing that professionals performed better in X-ray image inspection, but not the visual search task. However, there was no difference between students and professionals regarding the importance of the visual-cognitive abilities for either task. Because there is some freedom in the decision whether a suspicious object should be declared as a threat or as harmless, the results of X-ray image inspection in terms of hit and false alarm rate depend on the screeners’ response tendency. Manuscript 2 evaluated whether three commonly used detection measures – ${d}'$, ${A}'$, and ${d}_{a}$ – are a valid representation of detection performance that is independent from response tendency. The results were consistently in favor of da with a slope parameter of around 0.6. In Manuscript 3 it was further shown that screeners can change their response tendency to increase the detection of novel threats. Also, screeners with a high ability to recognize everyday objects detected more novel threats when their response tendency was manipulated. The thesis further addressed changes that screeners face due to technological developments. Manuscript 4 showed that screeners can inspect X-ray images for one hour straight without a decrease in performance under conditions of remote cabin baggage screening, which means that X-ray image inspection is performed in a quiet room remote from the checkpoint. These screeners did not show a lower performance, but reported more distress, compared to screeners who took a 10 min break after every 20 min of screening. Manuscript 5 evaluated detection systems for cabin baggage screening (EDSCB). EDSCB only increased the detection of improvised explosive devices (IEDs) for inexperienced screeners if alarms by the EDSCB were indicated on the image and the screeners had to decide whether a threat was present or not. The detection of mere explosives, which lack the triggering device of IEDs, was only increased if the screeners could not decide against an alarm by the EDSCB. Manuscript 6 used discrete event simulation to evaluate how EDSCB impacts the throughput of passenger baggage screening. Throughput decreased with increasing false alarm rate of the EDSCB. However, fast alarm resolution processes and screeners with a low false alarm rate increased throughput. Taken together, the present findings contribute to understanding X-ray image inspection as a task with a search and decision component. The findings provide insights into basic aspects like the required visual-cognitive abilities and valid measures of detection performance, but also into applied research questions like for how long X-ray image inspection can be performed and how automation can assist with the detection of explosives

Improving intrusion detection model prediction by threshold adaptation

This research was supported and funded by the Government of the Sultanate of Oman represented by the Ministry of Higher Education and the Sultan Qaboos University.Network traffic exhibits a high level of variability over short periods of time. This variability impacts negatively on the accuracy of anomaly-based network intrusion detection systems (IDS) that are built using predictive models in a batch learning setup. This work investigates how adapting the discriminating threshold of model predictions, specifically to the evaluated traffic, improves the detection rates of these intrusion detection models. Specifically, this research studied the adaptability features of three well known machine learning algorithms: C5.0, Random Forest and Support Vector Machine. Each algorithm’s ability to adapt their prediction thresholds was assessed and analysed under different scenarios that simulated real world settings using the prospective sampling approach. Multiple IDS datasets were used for the analysis, including a newly generated dataset (STA2018). This research demonstrated empirically the importance of threshold adaptation in improving the accuracy of detection models when training and evaluation traffic have different statistical properties. Tests were undertaken to analyse the effects of feature selection and data balancing on model accuracy when different significant features in traffic were used. The effects of threshold adaptation on improving accuracy were statistically analysed. Of the three compared algorithms, Random Forest was the most adaptable and had the highest detection rates.Publisher PDFPeer reviewe

An Integrated Cyber Security Risk Management Approach for a Cyber-Physical System

A cyber-physical system (CPS) is a combination of physical system components with cyber capabilities that have a very tight interconnectivity. CPS is a widely used technology in many applications, including electric power systems, communications, and transportation, and healthcare systems. These are critical national infrastructures. Cybersecurity attack is one of the major threats for a CPS because of many reasons, including complexity and interdependencies among various system components, integration of communication, computing, and control technology. Cybersecurity attacks may lead to various risks affecting the critical infrastructure business continuity, including degradation of production and performance, unavailability of critical services, and violation of the regulation. Managing cybersecurity risks is very important to protect CPS. However, risk management is challenging due to the inherent complex and evolving nature of the CPS system and recent attack trends. This paper presents an integrated cybersecurity risk management framework to assess and manage the risks in a proactive manner. Our work follows the existing risk management practice and standard and considers risks from the stakeholder model, cyber, and physical system components along with their dependencies. The approach enables identification of critical CPS assets and assesses the impact of vulnerabilities that affect the assets. It also presents a cybersecurity attack scenario that incorporates a cascading effect of threats and vulnerabilities to the assets. The attack model helps to determine the appropriate risk levels and their corresponding mitigation process. We present a power grid system to illustrate the applicability of our work. The result suggests that risk in a CPS of a critical infrastructure depends mainly on cyber-physical attack scenarios and the context of the organization. The involved risks in the studied context are both from the technical and nontechnical aspects of the CPS

Artificial Intelligence and Ambient Intelligence

This book includes a series of scientific papers published in the Special Issue on Artificial Intelligence and Ambient Intelligence at the journal Electronics MDPI. The book starts with an opinion paper on “Relations between Electronics, Artificial Intelligence and Information Society through Information Society Rules”, presenting relations between information society, electronics and artificial intelligence mainly through twenty-four IS laws. After that, the book continues with a series of technical papers that present applications of Artificial Intelligence and Ambient Intelligence in a variety of fields including affective computing, privacy and security in smart environments, and robotics. More specifically, the first part presents usage of Artificial Intelligence (AI) methods in combination with wearable devices (e.g., smartphones and wristbands) for recognizing human psychological states (e.g., emotions and cognitive load). The second part presents usage of AI methods in combination with laser sensors or Wi-Fi signals for improving security in smart buildings by identifying and counting the number of visitors. The last part presents usage of AI methods in robotics for improving robots’ ability for object gripping manipulation and perception. The language of the book is rather technical, thus the intended audience are scientists and researchers who have at least some basic knowledge in computer science

Differential evolution technique on weighted voting stacking ensemble method for credit card fraud detection

Differential Evolution is an optimization technique of stochastic search for a population-based vector, which is powerful and efficient over a continuous space for solving differentiable and non-linear optimization problems. Weighted voting stacking ensemble method is an important technique that combines various classifier models. However, selecting the appropriate weights of classifier models for the correct classification of transactions is a problem. This research study is therefore aimed at exploring whether the Differential Evolution optimization method is a good approach for defining the weighting function. Manual and random selection of weights for voting credit card transactions has previously been carried out. However, a large number of fraudulent transactions were not detected by the classifier models. Which means that a technique to overcome the weaknesses of the classifier models is required. Thus, the problem of selecting the appropriate weights was viewed as the problem of weights optimization in this study. The dataset was downloaded from the Kaggle competition data repository. Various machine learning algorithms were used to weight vote a class of transaction. The differential evolution optimization techniques was used as a weighting function. In addition, the Synthetic Minority Oversampling Technique (SMOTE) and Safe Level Synthetic Minority Oversampling Technique (SL-SMOTE) oversampling algorithms were modified to preserve the definition of SMOTE while improving the performance. Result generated from this research study showed that the Differential Evolution Optimization method is a good weighting function, which can be adopted as a systematic weight function for weight voting stacking ensemble method of various classification methods.School of ComputingM. Sc. (Computing