Computer-assisted interventions designed to remediate low numeracy and developmental dyscalculia (mathematical learning disability) have been utilised in preschools and kindergartens with some efficacy for over thirty years (Clements, 2002). A recent development in this field is ‘adaptive game’ technology, which adapts task difficulty online as children learn. The Number Race is the first such package for mathematics. Previous efficacy studies suggest its use results in an improvement in core measures of early numeracy, such as speed at enumerating 1-3 objects (subitizing) and comparison of numerals and groups of objects. The present study tested the efficacy of a new version of The Number Race (version 3.0) using New Zealand English and incorporating new instructional factors, in a younger population than most previously tested. Participants were twelve 5-to-7-year-old children and a typically developing control group matched on age and sex (n = 12). Following pre-testing using standardised tests and a computerised battery, children in the intervention group used The Number Race for twenty minutes each school night, for one month. Post-testing results showed that there was a significant improvement in counting and subitizing speed for the intervention group. Participants also became faster and more accurate at comparing numerals. There were no significant changes in standardised mathematics scores. The mental number line task did not show any significant differences before and after intervention but a wide variety of patterns and possible use of strategies were revealed. Overall, this new version of The Number Race seems to have modest effects in this population.

There is a strong synergy between the concepts of evolution and adaptation in software engineering: software adaptation refers to both the current software being adapted and to the evolution process that leads to the new adapted software. Evolution changes for the purpose of adaptation are usually made at development or compile time, and are meant to handle predictable situations in the form of software change requests. On the other hand, software may also change and adapt itself based on the changes in its environment. Such adaptive changes are usually dynamic, and are suitable for dealing with unpredictable or temporary changes in the software's operating environment. A promising solution for software adaptation is to develop self-adaptive software systems that can manage changes dynamically at runtime in a rapid and reliable way. One of the main advantages of self-adaptive software is its ability to manage the complexity that stems from highly dynamic and nondeterministic operating environments. If a self-adaptive software system has been engineered and used properly, it can greatly improve the cost-effectiveness of software change through its lifespan. However, in practice, many of the existing approaches towards self-adaptive software are rather expensive and may increase the overall system complexity, as well as subsequent future maintenance costs. This means that in many cases, self-adaptive software is not a good solution, because its development and maintenance costs are not paid off. The situation is even worse in the case of making current (legacy) systems adaptive. There are several factors that have an impact on the cost-effectiveness and usability of self-adaptive software; however the main objective of this thesis is to make a software system adaptive in a cost-effective way, while keeping the target adaptive software generic, usable, and evolvable, so as to support future changes. In order to effectively engineer and use self-adaptive software systems, in this thesis we propose a new conceptual model for identifying and specifying problem spaces in the context of self-adaptive software systems. Based on the foundations of this conceptual model, we propose a model-centric approach for engineering self-adaptive software by designing a generic adaptation framework and a supporting evolution process. This approach is particularly tailored to facilitate and simplify the process of evolving and adapting current (legacy) software towards runtime adaptivity. The conducted case studies reveal the applicability and effectiveness of this approach in bringing self-adaptive behaviour into non-adaptive applications that essentially demand adaptive behaviour to sustain.

This study investigates the concept of adaptivity in relation to the evolution of software and hence software embedded products. Whilst laying out the benefits of adaptivity in products, it discusses the potential future threats engendered by the actual change observed in the functionality principles of adaptive products. The discussion is based upon cybernetic theory which defines control technology in the 20th century anew. Accordingly, literature survey on cybernetic theory, evolution of software from conventional to adaptive structure is presented. The changes in the functionality principles of adaptive systems and the similarities that these changes show with living autonomous systems is also investigated. The roles of product and user are redefined in relation to changing control mechanisms. Then, the new direction that the conventional product-user relationship has taken with adaptive products is examined. Finally, the potential future threats this new direction might bring is discussed with the help of two control conflict situations.

A distributed online curriculum and courseware (DONC2) development model is proposed in this study. Collaborative courseware development teams which may work in distributed academic or private institutions who need to develop higher quality, reduced cost, on time products are the users of DONC2 development model. The related features from the disciplines of instructional design and software engineering were combined and concepts like usability, especially in terms of formative and summative evaluation, interoperability and reusability were integrated into the model. The research is conducted as a collective case study, including four cases with distinctive characteristics to reveal the several practices in online curriculum and courseware development work. The DONC2 development model was proposed using the results gathered from the investigated cases and a literature survey. The model uses the iterative incremental and agile software development approaches in order to overcome the disadvantages of other linear system development approaches. This enables building releasable products in short time periods with increased quality. Furthermore, continuous communication, evaluation and feedback as well as good project management and readiness to adapt to changes are integrated as the essential characteristics. DONC2 development model is different than previous linear and non-adaptive models in all of these aspects.

Our research posits a context-sensitive, adaptable, assistive services and technology system (CAAST) that takes advantage of the advancements in mobile computing to provide barrier-free access to environmental information and devices. To inform our research we explore the following topics: the deficiencies associated with current assistive technologies; the advances in wireless sensor node technology; the interference and accuracy problems associated with wireless location detection; the coordination problems associated with service discovery; the management and coordination problems associated with decentralized sensor nodes; the separation of information and activities from the human interface; the efficiency and abstraction problems associated with interface description languages; and the adaptation of information and activities to meet the needs of those with disabilities. As a result of our research into these areas we devise an assistive technology, CAAST, that intends to be a comprehensive approach to universal access to information and activities for those with disabilities. / Department of Computer Science

There is a strong synergy between the concepts of evolution and adaptation in software engineering: software adaptation refers to both the current software being adapted and to the evolution process that leads to the new adapted software. Evolution changes for the purpose of adaptation are usually made at development or compile time, and are meant to handle predictable situations in the form of software change requests. On the other hand, software may also change and adapt itself based on the changes in its environment. Such adaptive changes are usually dynamic, and are suitable for dealing with unpredictable or temporary changes in the software's operating environment. A promising solution for software adaptation is to develop self-adaptive software systems that can manage changes dynamically at runtime in a rapid and reliable way. One of the main advantages of self-adaptive software is its ability to manage the complexity that stems from highly dynamic and nondeterministic operating environments. If a self-adaptive software system has been engineered and used properly, it can greatly improve the cost-effectiveness of software change through its lifespan. However, in practice, many of the existing approaches towards self-adaptive software are rather expensive and may increase the overall system complexity, as well as subsequent future maintenance costs. This means that in many cases, self-adaptive software is not a good solution, because its development and maintenance costs are not paid off. The situation is even worse in the case of making current (legacy) systems adaptive. There are several factors that have an impact on the cost-effectiveness and usability of self-adaptive software; however the main objective of this thesis is to make a software system adaptive in a cost-effective way, while keeping the target adaptive software generic, usable, and evolvable, so as to support future changes. In order to effectively engineer and use self-adaptive software systems, in this thesis we propose a new conceptual model for identifying and specifying problem spaces in the context of self-adaptive software systems. Based on the foundations of this conceptual model, we propose a model-centric approach for engineering self-adaptive software by designing a generic adaptation framework and a supporting evolution process. This approach is particularly tailored to facilitate and simplify the process of evolving and adapting current (legacy) software towards runtime adaptivity. The conducted case studies reveal the applicability and effectiveness of this approach in bringing self-adaptive behaviour into non-adaptive applications that essentially demand adaptive behaviour to sustain.

Thesis (Ph. D.)--Electrical and Computer Engineering, Georgia Institute of Technology, 2006. / Schwan, Karsten, Committee Member ; Schimmel, David, Committee Member ; Copeland, John, Committee Member ; Owen, Henry, Committee Chair ; Wills, Linda, Committee Member.

As software evolves and becomes more complex, self-adaptive systems become a moreinteresting solution. Self-adaptive software systems are capable to perform changes inthemselves without human intervention. To make this possible it is necessary toperform a good observation of the system and its environment. This observation is madeby a monitoring system.In this paper, a framework for monitoring OSGi based applications is presented.OSGi is a module system and service platform for Java. This framework offers run-timeinformation about OSGi modules, services and their behavior.The first step is to make a state-of-the-art survey of existing methods to monitor inthe field of self-adaptive systems and OSGi based applications. The survey reviews aset of articles in the area. It is performed to discover what are the common objectivesand problems that any monitoring system faces. After that, the requirements for theframework are stated. These requirements specify the functionality that the frameworkis required to provide, along with the quality attributes that it has to meet. Todemonstrate use of the contributed monitoring framework, we have developed twoexample demonstrators. The objective of these demonstrators is to provide users of theframework with working examples, so that they can use the framework to develop theirown monitoring systems.

Unexpected changes occurring in complex and dynamic domains render supporting systems unsuited to the new conditions. Examples of such domains include business ecosystems, digital service ecosystems, manufacturing, transport, and city modelling. These are regarded as ecosystem domains. Multi-agent systems are seen as an appropriate technology for their support, yet they lack the required ability to adapt to unexpected changes. The research presented in this thesis aims to create a multi-agent system based in-silico model endowed with the capability of adaptation to unexpected changes occurring in ecosystem domains. The approach taken consists of applying adaptation properties of complex adaptive systems, such as natural ecosystems, to multi-agent systems to create one which can cope with unexpected changes. A dynamic agent-based ecosystem model called DAEM is formalised by combining characteristics of natural ecosystem and principles of adaptive multi-agent systems. A set of experiments is presented using a DAEM prototype to demonstrate its resilience to unexpected changes in a hypothetical ecosystem. A comparison is made against a simulated typical solution for showing how DAEM is more resilient to unexpected changes than the typical approach. This supports the claim of this thesis that DAEM represents a significant contribution to knowledge. A software embodiment of DAEM to drive adaptation in ecosystem domains is presented and placed in an execution context evaluated by two practical examples of ecosystem domains. These show how DAEM suggests interactions to the supporting system of the execution context, and incorporates taken decisions into the ecosystem regarding interactions with other individuals. This supports the claim that the DAEM software embodiment is suitable for providing adaptation support in ecosystem domains, thus representing another significant contribution of this thesis. The contributions to knowledge of this thesis are then a) a formal model of a dynamic agent-based ecosystem called DAEM; and b) a software embodiment of DAEM, called DAEM layer, to support adaptation in ecosystem domains. Future work includes further tests to analyse patterns and make estimations in existing ecosystems, among others.

Organisations such as governments and healthcare bodies are increasingly responsible for managing large amounts of personal information, and the increasing complexity of modern information systems is causing growing concerns about the protection of these assets from insider threats. Insider threats are very difficult to handle, because the insiders have direct access to information and are trusted by their organisations. The nature of insider privacy breaches varies with the organisation’s acceptable usage policy and the attributes of an insider. However, the level of risk that insiders pose depends on insider breach scenarios including their access patterns and contextual information, such as timing of access. Protection from insider threats is a newly emerging research area, and thus, only few approaches are available that systemise the continuous monitoring of dynamic insider usage characteristics and adaptation depending on the level of risk. The aim of this research is to develop a formal framework for an adaptive early warning and response system for insider privacy breaches within dynamic software systems. This framework will allow the specification of multiple policies at different risk levels, depending on event patterns, timing constraints, and the enforcement of adaptive response actions, to interrupt insider activity. Our framework is based on Usage Control (UCON), a comprehensive model that controls previous, ongoing, and subsequent resource usage. We extend UCON to include interrupt policy decisions, in which multiple policy decisions can be expressed at different risk levels. In particular, interrupt policy decisions can be dynamically adapted upon the occurrence of an event or over time. We propose a computational model that represents the concurrent behaviour of an adaptive early warning and response system in the form of statechart. In addition, we propose a Privacy Breach Specification Language (PBSL) based on this computational model, in which event patterns, timing constraints, and the triggered early warning level are expressed in the form of policy rules. The main features of PBSL are its expressiveness, simplicity, practicality, and formal semantics. The formal semantics of the PBSL, together with a model of the mechanisms enforcing the policies, is given in an operational style. Enforcement mechanisms, which are defined by the outcomes of the policy rules, influence the system state by mutually interacting between the policy rules and the system behaviour. We demonstrate the use of this PBSL with a case study from the e-government domain that includes some real-world insider breach scenarios. The formal framework utilises a tool that supports the animation of the enforcement and policy models. This tool also supports the model checking used to formally verify the safety and progress properties of the system over the policy and the enforcement specifications.