Lights, Camera, Action! Exploring Effects of Visual Distractions on Completion of Security Tasks

Human errors in performing security-critical tasks are typically blamed on the complexity of those tasks. However, such errors can also occur because of (possibly unexpected) sensory distractions. A sensory distraction that produces negative effects can be abused by the adversary that controls the environment. Meanwhile, a distraction with positive effects can be artificially introduced to improve user performance. The goal of this work is to explore the effects of visual stimuli on the performance of security-critical tasks. To this end, we experimented with a large number of subjects who were exposed to a range of unexpected visual stimuli while attempting to perform Bluetooth Pairing. Our results clearly demonstrate substantially increased task completion times and markedly lower task success rates. These negative effects are noteworthy, especially, when contrasted with prior results on audio distractions which had positive effects on performance of similar tasks. Experiments were conducted in a novel (fully automated and completely unattended) experimental environment. This yielded more uniform experiments, better scalability and significantly lower financial and logistical burdens. We discuss this experience, including benefits and limitations of the unattended automated experiment paradigm

An integrated approach to supply chain risk analysis

Despite the increasing attention that supply chain risk management is receiving by both researchers and practitioners, companies still lack a risk culture. Moreover, risk management approaches are either too general or require pieces of information not regularly recorded by organisations. This work develops a risk identification and analysis methodology that integrates widely adopted supply chain and risk management tools. In particular, process analysis is performed by means of the standard framework provided by the Supply Chain Operations Reference Model, the risk identification and analysis tasks are accomplished by applying the Risk Breakdown Structure and the Risk Breakdown Matrix, and the effects of risk occurrence on activities are assessed by indicators that are already measured by companies in order to monitor their performances. In such a way, the framework contributes to increase companies' awareness and communication about risk, which are essential components of the management of modern supply chains. A base case has been developed by applying the proposed approach to a hypothetical manufacturing supply chain. An in-depth validation will be carried out to improve the methodology and further demonstrate its benefits and limitations. Future research will extend the framework to include the understanding of the multiple effects of risky events on different processe

Subspace discovery for video anomaly detection

PhDIn automated video surveillance anomaly detection is a challenging task. We address this task as a novelty detection problem where pattern description is limited and labelling information is available only for a small sample of normal instances. Classification under these conditions is prone to over-fitting. The contribution of this work is to propose a novel video abnormality detection method that does not need object detection and tracking. The method is based on subspace learning to discover a subspace where abnormality detection is easier to perform, without the need of detailed annotation and description of these patterns. The problem is formulated as one-class classification utilising a low dimensional subspace, where a novelty classifier is used to learn normal actions automatically and then to detect abnormal actions from low-level features extracted from a region of interest. The subspace is discovered (using both labelled and unlabelled data) by a locality preserving graph-based algorithm that utilises the Graph Laplacian of a specially designed parameter-less nearest neighbour graph. The methodology compares favourably with alternative subspace learning algorithms (both linear and non-linear) and direct one-class classification schemes commonly used for off-line abnormality detection in synthetic and real data. Based on these findings, the framework is extended to on-line abnormality detection in video sequences, utilising multiple independent detectors deployed over the image frame to learn the local normal patterns and infer abnormality for the complete scene. The method is compared with an alternative linear method to establish advantages and limitations in on-line abnormality detection scenarios. Analysis shows that the alternative approach is better suited for cases where the subspace learning is restricted on the labelled samples, while in the presence of additional unlabelled data the proposed approach using graph-based subspace learning is more appropriate

Information on Resource Utilisation for Operational Planning in Port Hinterland Transport

To meet increased freight flows through maritime ports, a high level of resource utilisation in hinterland transport is of crucial importance. However, various perspectives on resource utilisation create issues with use of information for operational decisions in port hinterland. The purpose of this paper is to explore the use of information related to resource utilisation for operational planning in port hinterland freight transport to facilitate its improvement. The study is case-based, and the data is collected through semi-structured interviews, visual observations, and company documents. The findings are analysed with a framework built from literature emphasising different resource utilisation perspectives and the use of information in road freight transport chain decisions. The findings show that the use of information on resource utilisation in operational freight transport decisions in the port hinterland transport system is limited and lacks a complete system overview. Instead of the information on measured parameters, different types of estimates of efficiency parameters (including resource utilisation) are commonly used for operational planning decisions. The information about the measured indicators has to be combined with other information to obtain an efficient level of resource utilisation; otherwise, it could generate incorrect assumptions regarding utilisation. The paper contributes to the topic of operational freight transport planning by describing the use of information on resource utilisation

Embodied Evolution in Collective Robotics: A Review

This paper provides an overview of evolutionary robotics techniques applied to on-line distributed evolution for robot collectives -- namely, embodied evolution. It provides a definition of embodied evolution as well as a thorough description of the underlying concepts and mechanisms. The paper also presents a comprehensive summary of research published in the field since its inception (1999-2017), providing various perspectives to identify the major trends. In particular, we identify a shift from considering embodied evolution as a parallel search method within small robot collectives (fewer than 10 robots) to embodied evolution as an on-line distributed learning method for designing collective behaviours in swarm-like collectives. The paper concludes with a discussion of applications and open questions, providing a milestone for past and an inspiration for future research.Comment: 23 pages, 1 figure, 1 tabl

Motivation, Design, and Ubiquity: A Discussion of Research Ethics and Computer Science

Modern society is permeated with computers, and the software that controls them can have latent, long-term, and immediate effects that reach far beyond the actual users of these systems. This places researchers in Computer Science and Software Engineering in a critical position of influence and responsibility, more than any other field because computer systems are vital research tools for other disciplines. This essay presents several key ethical concerns and responsibilities relating to research in computing. The goal is to promote awareness and discussion of ethical issues among computer science researchers. A hypothetical case study is provided, along with questions for reflection and discussion.Comment: Written as central essay for the Computer Science module of the LANGURE model curriculum in Research Ethic

Job profiling: How artificial intelligence supports the management of complexity induced by product variety

Firms and supply chains (SC) increasingly are forced to customise products and optimise processes since today’s markets are, on average, more demanding in terms of both costs and customer satisfaction. Generally, when product variety (PV) increases not only improves sales performance, since products offered better fit customers’ expectations, but also increases the complexity in SC processes management, rising operational costs. For that reason, accurate management of product diversity is a fundamental point for the brands' success, which is why it is going to be investigated in that project. Moreover, firms’ managers apply strategies to mitigate or accommodate this complexity, avoiding the customer satisfaction and cost trade-off to remain competitive and survive. However, we were wondering if it is enough. Artificial Intelligence (AI) has emerged to stay. Digitalisation era, data availability, and the improvement in computing power have boomed AI’s potential in improving systems, controlling processes, and tackling complexity. These strengths are suitable to help managers not only to tackle the complexity arising from PV but also to boost the supply chain performance (SCP