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International Conference on Narrative and Interactive Learning Environments (5th : 2008 : Edinburgh, Scotland)

Australasian Conference on Interactive Entertainment (4th : 2007 : Melbourne, Victoria)

Australasian Conference on Interactive Entertainment (3rd : 2006 : Perth, Western Australia)

Traditional DL models are complex and resource hungry and thus, care needs to be taken in designing Internet of (medical) things (IoT, or IoMT) applications balancing efficiency-complexity trade-off. Recent IoT solutions tend to avoid using deep-learning methods due to such complexities, and rather classical filter-based methods are commonly used. We hypothesize that a shallow CNN model can offer satisfactory level of performance in combination by leveraging other essential solution-components, such as post-processing that is suitable for resource constrained environment. In an IoMT application context, QRS-detection and R-peak localisation from ECG signal as a case study, the complexities of CNN models and post-processing were varied to identify a set of combinations suitable for a range of target resource-limited environments. To the best of our knowledge, finding a deploy-able configuration, by incrementally increasing the CNN model complexity, as required to match the target's resource capacity, and leveraging the strength of post-processing, is the first of its kind. The results show that a shallow 2-layer CNN with a suitable post-processing can achieve $>$90\% F1-score, and the scores continue to improving for 8-32 layer CNNs, which can be used to profile target constraint environment. The outcome shows that it is possible to design an optimal DL solution with known target performance characteristics and resource (computing capacity, and memory) constraints.

Timely recognition of plant pests from field images is significant to avoid potential losses of crop yields. Traditional convolutional neural network-based deep learning models demand high computational capability and require large labelled samples for each pest type for training. On the other hand, the existing lightweight network-based approaches suffer in correctly classifying the pests because of common characteristics and high similarity between multiple plant pests. In this work, a novel double attention-based lightweight deep learning architecture is proposed to automatically recognize different plant pests. The lightweight network facilitates faster and small data training while the double attention module increases performance by focusing on the most pertinent information. The proposed approach achieves 96.61%, 99.08% and 91.60% on three variants of two publicly available datasets with 5869, 545 and 500 samples, respectively. Moreover, the comparison results reveal that the proposed approach outperforms existing approaches on both small and large datasets consistently., Comment: 14 pages

Automated QRS detection methods depend on the ECG data which is sampled at a certain frequency, irrespective of filter-based traditional methods or convolutional network (CNN) based deep learning methods. These methods require a selection of the sampling frequency at which they operate in the very first place. While working with data from two different datasets, which are sampled at different frequencies, often, data from both the datasets may need to resample at a common target frequency, which may be the frequency of either of the datasets or could be a different one. However, choosing data sampled at a certain frequency may have an impact on the model's generalisation capacity, and complexity. There exist some studies that investigate the effects of ECG sample frequencies on traditional filter-based methods, however, an extensive study of the effect of ECG sample frequency on deep learning-based models (convolutional networks), exploring their generalisability and complexity is yet to be explored. This experimental research investigates the impact of six different sample frequencies (50, 100, 250, 500, 1000, and 2000Hz) on four different convolutional network-based models' generalisability and complexity in order to form a basis to decide on an appropriate sample frequency for the QRS detection task for a particular performance requirement. Intra-database tests report an accuracy improvement no more than approximately 0.6\% from 100Hz to 250Hz and the shorter interquartile range for those two frequencies for all CNN-based models. The findings reveal that convolutional network-based deep learning models are capable of scoring higher levels of detection accuracies on ECG signals sampled at frequencies as low as 100Hz or 250Hz while maintaining lower model complexity (number of trainable parameters and training time).

In this paper, we propose a new mixed-integer linear programming (MILP) model ontology and a novel constraint typology of MILP formulations. MILP is a commonly used mathematical programming technique for modelling and solving real-life scheduling, routing, planning, resource allocation, and timetabling optimization problems providing optimized business solutions for industry sectors such as manufacturing, agriculture, defence, healthcare, medicine, energy, finance, and transportation. Despite the numerous real-life Combinatorial Optimization Problems found and solved and millions yet to be discovered and formulated, the number of types of constraints (the building blocks of a MILP) is relatively small. In the search for a suitable machine-readable knowledge representation structure for MILPs, we propose an optimization modelling tree built based upon an MILP model ontology that can be used as a guide for automated systems to elicit an MILP model from end-users on their combinatorial business optimization problems. Our ultimate aim is to develop a machine-readable knowledge representation for MILP that allows us to map an end-user's natural language description of the business optimization problem to an MILP formal specification as a first step towards automated mathematical modelling., Comment: 10 pages, 2 figures

This paper proposes a Mixed-Integer Linear Programming approach for the Soft Graph Clustering Problem. This is the first method that simultaneously allocates membership proportion for vertices that lie in multiple clusters, and that enforces an equal balance of the cluster memberships. Compared to ([Palla et al., 2005], [Derenyi et al., 2005], [Adamcsek et al., 2006]), the clusters found in our method are not limited to k-clique neighbourhoods. Compared to ([Hope and Keller, 2013]), our method can produce non-trivial clusters even for a connected unweighted graph.