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Abstract-Novel computing systems are increasingly being composed of large numbers of heterogeneous components, each with potentially different goals or local perspectives, and connected in networks which change over time. Management of such systems quickly becomes infeasible for humans. As such, future computing systems should be able to achieve advanced levels of autonomous behaviour. In this context, the system's ability to be self-aware and be able to self-express becomes important. This paper surveys definitions and current understanding of self-awareness and self-expression in biology and cognitive science. Subsequently, previous efforts to apply these concepts to computing systems are described. This has enabled the development of novel working definitions for selfawareness and self-expression within the context of computing systems.

In order for a neural network ensemble to generalise properly, two factors are considered vital. One is the diversity and the other is the accuracy of the networks that comprise the ensemble. There exists a tradeoff as to what should be the optimal measures of diversity and accuracy. The aim of this paper is to address this issue. We propose the DIVACE algorithm which tries to produce an ensemble as it searches for the optimum point on the diversity-accuracy curve. The DIVACE algorithm formulates the ensemble learning problem as a multi-objective problem explicitly.

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A dvanced computing systems generally contain many heterogeneous subsystems, each with a local perspective and goal set, which interconnect in changing network topologies. The subsystems must interact with each other and with humans in ways that are difficult to understand and predict while robustly maintaining performance, reliability, and security even with unforeseen dynamics, such as system failures or changing goals.To meet these stringent requirements, computational systemsranging from robot swarms and personal music devices to Web services and sensor networks-must achieve sophisticated autonomous behavior by adapting themselves at runtime and through learning processes that enable ongoing self-change. Managing tradeoffs among conflicting local and global goals at runtime requires considerable awareness of both the system's current state and its environment. Yet researchers have only recently begun to understand the implications of selfawareness principles and how to translate them into system engineering. Consequently, there is no general methodology for architecting self-aware systems or for comparing their self-awareness capabilities.To address this need, we examined how human selfawareness can serve as a source of inspiration for a new notion of computational self-awareness and associated self-expression, and we developed a general framework for describing a computing system's self-awareness properties. As part of this work, we created a reference architecture, which we used to derive architectural patterns for RESEARCH FEATURE