Full Interpretable Machine Learning Method with In-line Coordinates

This thesis explores a new approach for machine learning classification task in 2-dimensional space (2-D ML) with In-line Coordinates. This is a full machine learning approach that does not require to deal with n-dimensional data in n-dimensional space. In-line coordinates method allows discovering n-D patterns in 2-D space without loss of n-D information using graph representation of n-D data in 2-D. Specifically, this thesis shows that it can be done with In-line Based Coordinates in different modifications, which are defined, including static and dynamic ones. Some classification and regression algorithms based on these In-line Coordinates were explored. Two successful cases studies based on benchmark datasets (Wisconsin Breast Cancer dataset and Page Block Classification dataset) demonstrated the feasibility of the approach. This approach helps to consolidate further a whole new area of full 2-D machine learning with a respective methodology. In-line coordinates method has advantages to actively include the end-users into the discovering of models and their justification. Another advantage is providing interpretable ML models. Keywords— interpretable machine learning, classification, regression, visual knowledge discovery

Proactive and Dynamic Task Scheduling in Fog-cloud Environment

Fog computing was introduced for the first time by Cisco in 2012. Since then, there has been a great number of studies on fog computing, in which vacant and free-of-charge computing resources in local networks provide low-latency services to end devices. Even though traditional architecture with scalable and powerful central servers in cloud can accommodate those tasks, it is costly to allocate resources in cloud to execute all those tasks. In addition, it falls short of satisfying Quality of Service (QoS) requirements in terms of waiting time because of long distance communication between servers and user end devices. In this thesis, we discuss dynamic scheduling problem in fog-cloud collaboration environment for real-time applications when QoS is strict and when an answer is useless if the corresponding application finishes its execution after a pre-defined deadline. By taking into account an admission control procedure to grant only requests whose deadline requirements are feasible with respect to the available resources in the network, we study a proactive scenario using different strategies to calculate schedules and to assign resources, within the admission control procedure to accommodate an incoming request. Then, we propose our heuristic with four variants corresponding to four different strategies, with the adjustment of a trade-off cost-makespan factor in an utility function. When evaluating performance with some baseline methods in such proactive scenario, the numerical results show that our variants can meet deadline requirements for more applications while exploiting more efficiently the resources in the fog layer and being charged less for using cloud. Keywords: fog computing, cloud computing, dynamic scheduling, real-time scheduling, task scheduling, workflow applications, DAG, QoS requirements, heterogeneous systems

The role of information asymmetry and the level of market trading activity in shaping the time-to-maturity pattern of futures return volatility

I consider two explanations for the mixed empirical results on the Samuelson effect, which postulates that futures return volatility increases closer to maturity when the futures price becomes more sensitive to information flows. First, I empirically investigate Hong’s (2000) theoretical suggestion that information asymmetry has an impact on the time-to-maturity pattern of commodity futures return volatility (the “volatility pattern”) by testing the relationships information asymmetry has with the time-to-maturity and return volatility of commodity futures. I find that information asymmetry rises as commodity futures near maturity and that this increases return volatility. Thus, this “speculative effect” amplifies return volatility and can potentially be a more significant driver of the volatility pattern than Samuelson’s (1965) price elasticity effect. Second, I directly examine the time-to-maturity pattern of the sensitivity of futures return volatility to information flows (the “sensitivity pattern”) and find that it has an inverted U-shape. I point out that the results for tests of a linear volatility pattern are more significant when the inverted U-shape of the sensitivity pattern tilts more towards maturity. As an example of the practical implication of my findings, I show that a futures price series constructed based on contracts that are closest to the peak of the sensitivity pattern captures higher volatility (9.98% in-sample and 2.63% out-of-sample) than the often used closest-to-maturity series.Thesis (Ph.D.) -- University of Adelaide, Business School, 201