An Operationalized Model for Defining Computational Thinking

The Computational Thinking (CT) conceptual framework is entering its second decade of research yet still lacks a cohesive definition by which the field can coalesce. The lack of clear definition makes assessment tool challenging to formulate, pedagogical efforts difficult to compare, and research difficult to synthesize. This paper looks to operationalize differing definitions of CT enhancing the ability to teach then assess the presence of CT. Expanding upon CT definitions, industry practices and processes, and educational theory, we link existing concepts and propose a new element to model an active definition of CT as a theoretical framework to guide future research. Our model updates existing CT definition by formally including Modeling, introducing Socio-Technical processes, separating Information Gathering from Data Collection and adding emphasis to Testing as a vital CT concept. We feel these elements and interconnections make CT is easier to describe and measure

Hierarchical generative modelling for autonomous robots

Humans generate intricate whole-body motions by planning, executing and combining individual limb movements. We investigated this fundamental aspect of motor control and approached the problem of autonomous task completion by hierarchical generative modelling with multi-level planning, emulating the deep temporal architecture of human motor control. We explored the temporal depth of nested timescales, where successive levels of a forward or generative model unfold, for example, object delivery requires both global planning and local coordination of limb movements. This separation of temporal scales suggests the advantage of hierarchically organizing the global planning and local control of individual limbs. We validated our proposed formulation extensively through physics simulation. Using a hierarchical generative model, we showcase that an embodied artificial intelligence system, a humanoid robot, can autonomously complete a complex task requiring a holistic use of locomotion, manipulation and grasping: the robot adeptly retrieves and transports a box, opens and walks through a door, kicks a football and exhibits robust performance even in the presence of body damage and ground irregularities. Our findings demonstrated the efficacy and feasibility of human-inspired motor control for an embodied artificial intelligence robot, highlighting the viability of the formulized hierarchical architecture for achieving autonomous completion of challenging goal-directed tasks

An investigation into the unsoundness of static program analysis : a thesis presented in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Computer Science at Massey University, Palmerston North, New Zealand

Static program analysis is widely used in many software applications such as in security analysis, compiler optimisation, program verification and code refactoring. In contrast to dynamic analysis, static analysis can perform a full program analysis without the need of running the program under analysis. While it provides full program coverage, one of the main issues with static analysis is imprecision -- i.e., the potential of reporting false positives due to overestimating actual program behaviours. For many years, research in static program analysis has focused on reducing such imprecision while improving scalability. However, static program analysis may also miss some critical parts of the program, resulting in program behaviours not being reported. A typical example of this is the case of dynamic language features, where certain behaviours are hard to model due to their dynamic nature. The term ``unsoundness'' has been used to describe those missed program behaviours. Compared to static analysis, dynamic analysis has the advantage of obtaining precise results, as it only captures what has been executed during run-time. However, dynamic analysis is also limited to the defined program executions. This thesis investigates the unsoundness issue in static program analysis. We first investigate causes of unsoundness in terms of Java dynamic language features and identify potential usage patterns of such features. We then report the results of a number of empirical experiments we conducted in order to identify and categorise the sources of unsoundness in state-of-the-art static analysis frameworks. Finally, we quantify and measure the level of unsoundness in static analysis in the presence of dynamic language features. The models developed in this thesis can be used by static analysis frameworks and tools to boost the soundness in those frameworks and tools

Functional programming abstractions for weakly consistent systems

In recent years, there has been a wide-spread adoption of both multicore and cloud computing. Traditionally, concurrent programmers have relied on the underlying system providing strong memory consistency, where there is a semblance of concurrent tasks operating over a shared global address space. However, providing scalable strong consistency guarantees as the scale of the system grows is an increasingly difficult endeavor. In a multicore setting, the increasing complexity and the lack of scalability of hardware mechanisms such as cache coherence deters scalable strong consistency. In geo-distributed compute clouds, the availability concerns in the presence of partial failures prohibit strong consistency. Hence, modern multicore and cloud computing platforms eschew strong consistency in favor of weakly consistent memory, where each task\u27s memory view is incomparable with the other tasks. As a result, programmers on these platforms must tackle the full complexity of concurrent programming for an asynchronous distributed system. ^ This dissertation argues that functional programming language abstractions can simplify scalable concurrent programming for weakly consistent systems. Functional programming espouses mutation-free programming, and rare mutations when present are explicit in their types. By controlling and explicitly reasoning about shared state mutations, functional abstractions simplify concurrent programming. Building upon this intuition, this dissertation presents three major contributions, each focused on addressing a particular challenge associated with weakly consistent loosely coupled systems. First, it describes A NERIS, a concurrent functional programming language and runtime for the Intel Single-chip Cloud Computer, and shows how to provide an efficient cache coherent virtual address space on top of a non cache coherent multicore architecture. Next, it describes RxCML, a distributed extension of MULTIMLTON and shows that, with the help of speculative execution, synchronous communication can be utilized as an efficient abstraction for programming asynchronous distributed systems. Finally, it presents QUELEA, a programming system for eventually consistent distributed stores, and shows that the choice of correct consistency level for replicated data type operations and transactions can be automated with the help of high-level declarative contracts