Hierarchical Task Network Planning with Common-Sense Reasoning for Multiple-People Behaviour Analysis

Safety on public transport is a major concern for the relevant authorities. We
address this issue by proposing an automated surveillance platform which combines data from video, infrared and pressure sensors. Data homogenisation and integration is achieved by a distributed architecture based on communication middleware that resolves interconnection issues, thereby enabling data modelling. A common-sense knowledge base models and encodes knowledge about public-transport platforms and the actions and activities of passengers. Trajectory data from passengers is modelled as a time-series of human activities. Common-sense knowledge and rules are then applied to detect inconsistencies or errors in the data interpretation. Lastly, the rationality that characterises human behaviour is also captured here through a bottom-up Hierarchical Task Network planner that, along with common-sense, corrects misinterpretations to explain passenger behaviour. The system is validated using a simulated bus saloon scenario as a case-study. Eighteen video sequences were recorded with up to six passengers. Four metrics were used to evaluate performance. The system, with an accuracy greater than 90% for each of the four metrics, was found to outperform a rule-base system and a system containing planning alone.

Contextual anomaly detection in crowded surveillance scenes

This work addresses the problem of detecting human behavioural anomalies in crowded surveillance environments. We focus in particular on the problem of detecting subtle anomalies in a behaviourally heterogeneous surveillance scene. To reach this goal we implement a novel unsupervised context-aware process. We propose and evaluate a method of utilising social context and scene context to improve behaviour analysis. We find that in a crowded scene the application of Mutual Information based social context permits the ability to prevent self-justifying groups and propagate anomalies in a social network, granting a greater anomaly detection capability. Scene context uniformly improves the detection of anomalies in both datasets. The strength of our contextual features is demonstrated by the detection of subtly abnormal behaviours, which otherwise remain indistinguishable from normal behaviour.

Early warning systems for cyber defence

Cybercriminals ramp up their efforts with sophisticated techniques while defenders gradually update their typical security measures. Attackers often have a long-term interest in their targets. Due to a number of factors such as scale, architecture and nonproductive traffic however it makes difficult to detect them using typical intrusion detection techniques. Cyber early warning systems (CEWS) aim at alerting such attempts in their nascent stages using preliminary indicators. Design and implementation of such systems involves numerous research challenges such as generic set of indicators, intelligence gathering, uncertainty reasoning and information fusion. This paper discusses such challenges and presents the reader with compelling motivation. A carefully deployed empirical analysis using a real world attack scenario and a real network traffic capture is also presented.

Applied behavior analysis: What do teachers of students with autism spectrum disorder know?

With rising numbers of school-aged children with autism educated in mainstream classroomsand applied behavior analysis (ABA) considered the basis of best practice, teachers’ knowledgein this field has become a key concern for inclusion. Self-reported knowledge of ABA of specialneeds teachers (n=165) was measured and compared to their actual knowledge of ABAdemonstrated in accurate responses to a multiple-choice test. Findings reported here show thatteachers’ self-perceived knowledge exceeded actual knowledge and that actual knowledge ofABA was not

FPGA-based soft-core processors for image processing applications

With security and surveillance, there is an increasing need to process image data efficiently and effectively either at source or in a large data network. Whilst a Field-Programmable Gate Array (FPGA) has been seen as a key technology for enabling this, the design process has been viewed as problematic in terms of the time and effort needed for implementation and verification. The work here proposes a different approach of using optimized FPGA-based soft-core processors which allows the user to exploit the task and data level parallelism to achieve the quality of dedicated FPGA implementations whilst reducing design time. The paper also reports some preliminary
progress on the design flow to program the structure. An implementation for a Histogram of Gradients algorithm is also reported which shows that a performance of 328 fps can be achieved with this design approach, whilst avoiding the long design time, verification and debugging steps associated with conventional FPGA implementations.

Human behaviour recognition in data-scarce domains

This paper presents the novel theory for performing multi-agent activity recognition without requiring large training corpora. The reduced need for data means that robust probabilistic recognition can be performed within domains where annotated datasets are traditionally unavailable. Complex human activities are composed from sequences of underlying primitive activities. We do not assume that the exact temporal ordering of primitives is necessary, so can represent complex activity using an unordered bag. Our three-tier architecture comprises low-level video tracking, event analysis and high-level inference. High-level inference is performed using a new, cascading extension of the Rao–Blackwellised Particle Filter. Simulated annealing is used to identify pairs of agents involved in multi-agent activity. We validate our framework using the benchmarked PETS 2006 video surveillance dataset and our own sequences, and achieve a mean recognition F-Score of 0.82. Our approach achieves a mean improvement of 17% over a Hidden Markov Model baseline.

STPA-SafeSec: Safety and Security Analysis for Cyber-Physical Systems

Cyber-physical systems tightly integrate physical processes and information and communication technologies. As today’s critical infrastructures, e.g., the power grid or water distribution networks, are complex cyber-physical systems, ensuring their safety and security becomes of paramount importance. Traditional safety analysis methods, such as HAZOP, are ill-suited to assess these systems. Furthermore, cybersecurity vulnerabilities are often not considered critical, because their effects on the physical processes are not fully understood. In this work, we present STPA-SafeSec, a novel analysis methodology for both safety and security. Its results show the dependencies between cybersecurity vulnerabilities and system safety. Using this information, the most effective mitigation strategies to ensure safety and security of the system can be readily identified. We apply STPA-SafeSec to a use case in the power grid domain, and highlight its benefits.

Combining histology, stable isotope analysis and ZooMS collagen fingerprinting to investigate the taphonomic history and dietary behaviour of extinct giant tortoises from the Mare aux Songes deposit on Mauritius

Taphonomic research of bones can provide additional insight into a site's formation and development, the burial environment and ongoing post-mortem processes. A total of 30 tortoise (Cylindraspis) femur bone samples from the Mare aux Songes site (Mauritius)were studied histologically, assessing parameters such as presence and type of microbial alteration, inclusions, staining/infiltrations, the degree of microcracking and birefringence. The absence of microbial attack in the 4200 year old Mare aux Songes bones suggests the animals rapidly entered the soil whole-bodied and were sealed anoxically, although they suffered frombiological and chemical degradation (i.e. pyrite formation/oxidation, mineral dissolution and staining) related to changes in the site's hydrology. Additionally, carbon and nitrogen stable isotopeswere analysed to obtain information on the animals' feeding behaviour. The results show narrowly distributed δ13C ratios, indicating a terrestrial C3 plant-based diet, combined with a wide range in δ15N ratios. This is most likely related to the tortoises' drought-adaptive ability to change their metabolic processes, which can affect the δ15N ratios. Furthermore, ZooMS collagen fingerprinting analysis successfully identified two tortoise species (C. triserrata and C. inepta) in the bone assemblage,which,when combined with stable isotope data, revealed significantly different δ15N ratios between the two tortoise species. As climatic changes around this period resulted in increased aridity in the Mascarene Islands, this could explain the extremely elevated δ15N ratio in our dataset. The endemic fauna was able to endure the climatic changes 4200 years ago, although human arrival in the 17th century changed the original habitat to such an extent that it resulted in the extinction of several species. Fortunately we are still able to study these extinct tortoises due to the beneficial conditions of their burial environment, resulting in excellent bone preservation.