Evidential event inference in transport video surveillance

This paper presents a new framework for multi-subject event inference in surveillance video, where measurements produced by low-level vision analytics usually are noisy, incomplete or incorrect. Our goal is to infer the composite events undertaken by each subject from noise observations. To achieve this, we consider the temporal characteristics of event relations and propose a method to correctly associate the detected events with individual subjects. The Dempster–Shafer (DS) theory of belief functions is used to infer events of interest from the results of our vision analytics and to measure conflicts occurring during the event association. Our system is evaluated against a number of videos that present passenger behaviours on a public transport platform namely buses at different levels of complexity. The experimental results demonstrate that by reasoning with spatio-temporal correlations, the proposed method achieves a satisfying performance when associating atomic events and recognising composite events involving multiple subjects in dynamic environments.

A Steganalysis System Utilizing Temporal Pixel Correlation Of HEVC Video

High Efficiency Video Coding (HEVC) is the most recent video codec coming after currently most popular H.264/MPEG4 codecs and has promising compression capabilities. It is conjectured that it will be a substitute for current video compression standards. However, to the best knowledge of the authors, none of the current video steganalysis methods designed or tested with HEVC video. In this paper, pixel domain steganography applied on HEVC video is targeted for the first time. Also, its the first paper that employs accordion unfolding transformation, which merges temporal and spatial correlation, in pixel domain video steganalysis. With help of the transformation, temporal correlation is incorporated into the system. Its demonstrated for three different feature sets that integrating temporal dependency substantially increased the detection accuracy.

A Correlation Between Host Star Activity and Planet Mass for Close-in Extrasolar Planets?

The activity levels of stars are influenced by several stellar properties, such as stellar rotation, spectral type, and the presence of stellar companions. Analogous to binaries, planetary companions are also thought to be able to cause higher activity levels in their host stars, although at lower levels. Especially in X-rays, such influences are hard to detect because coronae of cool stars exhibit a considerable amount of intrinsic variability. Recently, a correlation between the mass of close-in exoplanets and their host star's X-ray luminosity has been detected, based on archival X-ray data from the ROSAT All-Sky Survey. This finding has been interpreted as evidence for star-planet interactions. We show in our analysis that this correlation is caused by selection effects due to the flux limit of the X-ray data used and due to the intrinsic planet detectability of the radial velocity method, and thus does not trace possible planet-induced effects. We also show that the correlation is not present in a corresponding complete sample derived from combined XMM-Newton and ROSAT data.

Polybius’ ‘previous landslide’: Proof that Hannibal’s invasion route crossed the col de la traversette

Both Polybius and Livy described a landslide/landslip that blocked the Punic Army’s exfiltration from a high col on the water divide in the Western Alps. The landslide, more aptly termed rockfall, has been a source of contention amongst classicists for centuries despite the fact that only two cols—Clapier and Traversette—exhibit rockfall debris on the lee side of the Alps. While the Clapier rockfall is too small and too young to have provided blockage, the Traversette debris is nearly as Polybius described it when he retraced the invasion route some 60 years after the event. His ‘two-tier’ description of the deposit, a doublet of younger and older rock rubble, including measurements of width and volume are close to modern measurements and prove that he knew, in advance, the route Hannibal had followed. It would take a practiced eye to correctly identify the stratigraphic complexity inherent in the Traversette Rockfall. Here we present weathering ratios, soil stratigraphic, mineral, chemical and microbiological evidence in support of Polybius’ observations as a considerable background database for future geoarchaeological exploration.

LS-ADT: Lightweight and Scalable Anomaly Detection for Cloud Datacentres

Cloud data centres are implemented as large-scale clusters with demanding requirements for service performance, availability and cost of operation. As a result of scale and complexity, data centres typically exhibit large numbers of system anomalies resulting from operator error, resource over/under provisioning, hardware or software failures and security issus anomalies are inherently difficult to identify and resolve promptly via human inspection. Therefore, it is vital in a cloud system to have automatic system monitoring that detects potential anomalies and identifies their source. In this paper we present a lightweight anomaly detection tool for Cloud data centres which combines extended log analysis and rigorous correlation of system metrics, implemented by an efficient correlation algorithm which does not require training or complex infrastructure set up. The LADT algorithm is based on the premise that there is a strong correlation between node level and VM level metrics in a cloud system. This correlation will drop significantly in the event of any performance anomaly at the node-level and a continuous drop in the correlation can indicate the presence of a true anomaly in the node. The log analysis of LADT assists in determining whether the correlation drop could be caused by naturally occurring cloud management activity such as VM migration, creation, suspension, termination or resizing. In this way, any potential anomaly alerts are reasoned about to prevent false positives that could be caused by the cloud operator’s activity. We demonstrate LADT with log analysis in a Cloud environment to show how the log analysis is combined with the correlation of systems metrics to achieve accurate anomaly detection.

Event-driven implicit authentication for mobile access control

In order to protect user privacy on mobile devices, an event-driven implicit authentication scheme is proposed in this paper. Several methods of utilizing the scheme for recognizing legitimate user behavior are investigated. The investigated methods compute an aggregate score and a threshold in real-time to determine the trust level of the current user using real data derived from user interaction with the device. The proposed scheme is designed to: operate completely in the background, require minimal training period, enable high user recognition rate for implicit authentication, and prompt detection of abnormal activity that can be used to trigger explicitly authenticated access control. In this paper, we investigate threshold computation through standard deviation and EWMA (exponentially weighted moving average) based algorithms. The result of extensive experiments on user data collected over a period of several weeks from an Android phone indicates that our proposed approach is feasible and effective for lightweight real-time implicit authentication on mobile smartphones.

How to return to “normal” after a cardiac event: State of the Art Lecture

Electronic report

Real-Time Multiple Event Detection and Classification Using Moving Window PCA

This paper proposes a method for the detection and classification of multiple events in an electrical power system in real-time, namely; islanding, high frequency events (loss of load) and low frequency events (loss of generation). This method is based on principal component analysis of frequency measurements and employs a moving window approach to combat the time-varying nature of power systems, thereby increasing overall situational awareness of the power system. Numerical case studies using both real data, collected from the UK power system, and simulated case studies, constructed using DigSilent PowerFactory, for islanding events, as well as both loss of load and generation dip events, are used to demonstrate the reliability of the proposed method.