Advances in Learning Bayesian Networks of Bounded Treewidth

This work presents novel algorithms for learning Bayesian networks of bounded treewidth. Both exact and approximate methods are developed. The exact method combines mixed integer linear programming formulations for structure learning and treewidth computation. The approximate method consists in sampling k-trees (maximal graphs of treewidth k), and subsequently selecting, exactly or approximately, the best structure whose moral graph is a subgraph of that k-tree. The approaches are empirically compared to each other and to state-of-the-art methods on a collection of public data sets with up to 100 variables.

Application of Evidence Theory and Discounting Techniques to Aerospace Design

Critical decisions are made by decision-makers throughout
the life-cycle of large-scale projects. These decisions are crucial as they
have a direct impact upon the outcome and the success of projects. To aid
decision-makers in the decision making process we present an evidential
reasoning framework. This approach utilizes the Dezert-Smarandache
theory to fuse heterogeneous evidence sources that suffer from levels
of uncertainty, imprecision and conflicts to provide beliefs for decision
options. To analyze the impact of source reliability and priority upon
the decision making process, a reliability discounting technique and a
priority discounting technique, are applied. A maximal consistent subset
is constructed to aid in dening where discounting should be applied.
Application of the evidential reasoning framework is illustrated using a
case study based in the Aerospace domain.

New FMO Type to Flag ROI in H264/AVC

This paper presents a new type of Flexible Macroblock Ordering (FMO) type for the H.264 Advanced Video Coding (AVC) standard, which can more efficiently flag the position and shape of regions of interest (ROIs) in each frame. In H.264/AVC, 7 types of FMO have been defined, all of which are designed for error resilience. Most previous work related to ROI processing has adopted Type-2 (foreground & background), or Type-6 (explicit), to flag the position and shape of the ROI. However, only rectangular shapes are allowed in Type-2 and for non-rectangular shapes, the non-ROI macroblocks may be wrongly flagged as being within the ROI, which could seriously affect subsequent processing of the ROI. In Type-6, each macroblock in a frame uses fixed-length bits to indicate to its slice group. In general, each ROI is assigned to one slice group identity. Although this FMO type can more accurately flag the position and shape of the ROI, it incurs a significant bitrate overhead. The proposed new FMO type uses the smallest rectangle that covers the ROI to indicate its position and a spiral binary mask is employed within the rectangle to indicate the shape of the ROI. This technique can accurately flag the ROI and provide significantly savings in the bitrate overhead. Compared with Type-6, an 80% to 90% reduction in the bitrate overhead can be obtained while achieving the same accuracy.

From Sensing to Molecular Logic: A Journey

A personal account of the establishment of luminescent PET (photoinduced electron transfer) sensing and its development into molecular logic is given. Several applications of these two research areas, e.g. blood electrolyte diagnostics, ‘lab-on-amolecule’ systems and molecular computational identification (MCID) are illustrated.

Gait period estimation algorithm based on the angles of extremities

Gait period estimation is an important step in the gait recognition framework. In this paper, we propose a new gait cycle detection method based on the angles of extreme points of both legs. In addition to that, to further improve the estimation of the gait period, the proposed algorithm divides the gait sequence into sections before identifying the maximum values. The proposed algorithm is scale invariant and less dependent on the silhouette shape. The performance of the proposed method was evaluated using the OU-ISIR speed variation gait database. The experimental results show that the proposed method achieved 90.2% gait recognition accuracy and outperforms previous methods found in the literature with the second best only achieved 67.65% accuracy.

Detecting Packed Executables using Steganalysis

This paper proposes a novel method of detecting packed executable files using steganalysis, primarily targeting the detection of obfuscated malware through packing. Considering that over 80% of malware in the wild is packed, detection accuracy and low false negative rates are important properties of malware detection methods. Experimental results outlined in this paper reveal that the proposed approach achieving an overall detection accuracy of greater than 99%, a false negative rate of 1% and a false positive rate of 0%.

Software defined FFT architecture for IEEE 802.11ac

The upcoming IEEE 802.11ac standard boosts the throughput of previous IEEE 802.11n by adding wider 80 MHz and 160 MHz channels with up to 8 antennas (versus 40 MHz channel and 4 antennas in 802.11n). This necessitates new 1-8 stream 256/512-point Fast Fourier Transform (FFT) / inverse FFT (IFFT) processing with 80/160 MSample/s throughput. Although there are abundant related work, they all fail to meet the requirements of IEEE 802.11ac FFT/IFFT on point size, throughput and multiple data streams at the same time. This paper proposes the first software defined FFT/IFFT architecture as a solution. By making use of a customised soft stream processor on FPGA, we show how a software defined FFT architecture can meet all the requirements of IEEE 802.11ac with low cost and high resource efficiency. When compared with dedicated Xilinx FFT core, our implementation exhibits only one third of the resources also up to three times of resource efficiency.

Learning Bayesian Networks with Thousands of Variables

We present a method for learning Bayesian networks from data sets containing thousands of variables without the need for structure constraints. Our approach is made of two parts. The first is a novel algorithm that effectively explores the space of possible parent sets of a node. It guides the exploration towards the most promising parent sets on the basis of an approximated score function that is computed in constant time. The second part is an improvement of an existing ordering-based algorithm for structure optimization. The new algorithm provably achieves a higher score compared to its original formulation. Our novel approach consistently outperforms the state of the art on very large data sets.

Fetal umbilical artery Doppler pulsatility index and childhood neurocognitive outcome at 12 years

OBJECTIVE: To determine whether an elevated fetal umbilical artery Doppler (UAD) pulsatility index (PI) at 28 weeks' gestation, in the absence of fetal growth restriction (FGR) and prematurity, is associated with adverse neurocognitive outcome in children aged 12 years.

METHODS: Prospective cohort study, comparing children with a normal fetal UAD PI (<90th centile) (n=110) and those with an elevated PI (≥90th centile) (n=40). UAD was performed at 28, 32 and 34 weeks gestation. At 12 years of age, all children were assessed under standardised conditions at Queen's University, Belfast, UK to determine cognitive and behavioural outcomes using the British Ability Score-II and Achenbach Child Behavioural Checklist Parent Rated Version under standardised conditions. Regression analysis was performed, controlling for confounders such as gender, socioeconomic status and age at assessment.

RESULTS: The mean age of follow-up was 12.4 years (±0.5 SD) with 44% of children male (n=63). When UAD was assessed at 28 weeks, the elevated fetal UAD group had lower scores in cognitive assessments of information processing and memory. Parameters included (1) recall of objects immediate verbal (p=0.002), (2) delayed verbal (p=0.008) and (3) recall of objects immediate spatial (p=0.0016). There were no significant differences between the Doppler groups at 32 or 34 weeks' gestation.

CONCLUSIONS: An elevated UAD PI at 28 weeks' gestation in the absence of FGR or prematurity is associated with lower scores of declarative memory in children aged 12 years. A potential explanation for this is an element of placental insufficiency in the presence of the appropriately grown fetus, which affects the development of the fetal hippocampus and information processing and memory long-term. These findings, however, had no impact on overall academic ability, mental processing and reasoning or overall behavioural function.