Mass Customisation Along Lifecycle of Autonomic Homes

Autonomic homes adapt themselves to give the user the best possible experience of the services they provide. They dynamically adapt its behavior at run-time in response to changing conditions in end-user needs and the surrounding environment devices. From the development point of view, producing and maintaining a large amount of autonomic homes need an affordable solution such as dynamic software product lines (DSPL). DSPL produce a set of products that share features and have the ability of reconfiguring at runtime. Since users maintain and modify their preferences in opportunistic and improvisational ways, an autonomic home must evolve in time according to user expectations. Current DSPL architectures implement the ability of recon- figuring a product but ignore user preferences. We present an extension to our DSPL architecture to incorporate user preferences so user customisation of autonomic homes is not limited to installation time but all along the lifetime

Neural mechanisms of visual awareness and their modulation by social threat

The human brain can extract an enormous wealth of visual information from our surroundings. However, only a fraction of this immense data set ever becomes available to the observer’s awareness. How and why certain information is selected for awareness are questions under active investigation. Following two introductory chapters, this thesis contains six inter-related experimental chapters, through which I will explore two key outstanding questions in this field, using bistable perceptual paradigms to study conscious and non-conscious visual processing in healthy human volunteers. The first major theme focuses on adding new insight into the brain regions and networks that facilitate transfer between non-conscious and conscious modes of visual processing. In Chapters 3 and 4 I will use fMRI, both in task-related and resting-state conditions, to delineate areas, and their interactions (in terms of effective connectivity), which are relevant for transition between different conscious perceptual experiences. In Chapter 5 I will focus on one specific region in the proposed perceptual transition-related network (the frontal eye field) and explore its causal role in access to awareness using repetitive TMS. The second key question explored in this thesis is how social cues in the visual environment influence non-conscious visual processing as well as transition to conscious vision. In Chapter 6 I will study behavioural effects of non-conscious social cues from faces, and the relationship of these effects to focal brain anatomy. Based on finding slower perceptuomotor performance when non-conscious faces contain threatening cues in Chapter 6, I hypothesise that a defensive freezing response is engaged in such situations. The final two experimental chapters will explore the correlates of putative human freezing in the context of non-conscious social threat: using fMRI and psychophysiological measurements to study effects on perceptual transition in Chapter 7, and relating TMS-induced motor-evoked potentials and concurrent psychophysiological measurements to non-conscious perceptuomotor performance in Chapter 8. Taken together, the presented findings shed new light on the network of brain regions involved in transition between non-conscious and conscious modes of visual processing. In addition, they uncover novel mechanisms through which socially relevant visual cues shape our awareness of the visual world, with particular emphasis on the engagement of defensive responses by socially threatening stimuli. The concluding chapter discusses the implications of these findings and explores relevant avenues for future work

Realising Variability in Dynamic Software Product Line Solutions

Modern systems need to be able to self-adapt to changes in user needs, and changes affecting the system itself or its environment. Dynamic software product line (DSPL) is an engineering approach for developing self-adaptive systems based on commonalities and variabilities for a family of similar systems. Currently, many DSPL approaches fail to meet all adaptability requirements, and in many cases, they are developed in a such unstructured manner that the controller is not explicitly represented, for example. We specify a two-dimension taxonomy to address basic technical issues for realising variability in DSPLs. The self-adaptation dimension classifies the different design choices for the adaptability requirements. The DSPL variability dimension classifies different design choices for implementing variability schemes and for creating different kinds of feature models. Our study was substantiated by surveying several DSPL approaches, and evaluating and comparing their different design strategies. We also summarise practical issues and difficulties, identify major trends in actual DSPL proposals, and suggest directions for future

Using Domain Features to Handle Feature Interactions

International audienceSoftware Product Lines in general and feature diagrams in particular support the modeling of software variability. Unfortunately, features may interact with each other, leading to feature interaction issues. Even if detected at the implementation level, interaction resolution choices are part of the business variability. In this paper, we propose to use a stepwise process to deal with feature interactions at the domain level: the way an interaction is resolved is considered as a variation point in the configuration process. This method and the associated implementation are applied onto a concrete case study (the jSeduite information system)

ETHOM: An Evolutionary Algorithm for Optimized Feature Models Generation (v. 1.2): Technical Report ISA-2012-TR-05

A feature model defines the valid combinations of features in a domain. The automated extraction of information from feature models is a thriving topic involving numerous analysis operations, techniques and tools. The progress of this discipline is leading to an increasing concern to test and compare the performance of analysis solutions using tough input models that show the behaviour of the tools in extreme situations (e.g. those producing longest execution times or highest memory consumption). Currently, these feature models are generated randomly ignoring the internal aspects of the tools under tests. As a result, these only provide a rough idea of the behaviour of the tools with average problems and are not sufficient to reveal their real strengths and weaknesses. In this technical report, we model the problem of finding computationally– hard feature models as an optimization problem and we solve it using a novel evolutionary algorithm. Given a tool and an analysis operation, our algorithm generates input models of a predefined size maximizing aspects as the execution time or the memory consumption of the tool when performing the operation over the model. This allows users and developers to know the behaviour of tools in pessimistic cases providing a better idea of their real power. Experiments using our evolutionary algorithm on a number of analysis operations and tools have successfully identified input models causing much longer executions times and higher memory consumption than random models of identical or even larger size. Our solution is generic and applicable to a variety of optimization problems on feature models, not only those involving analysis operations. In view of the positive results, we expect this work to be the seed for a new wave of research contributions exploiting the benefit of evolutionary programming in the field of feature modelling

A Dynamic Software Product Line Approach for Planning and Execution of Reconfigurations in Self-Adaptive Systems

Model-based autonomic computing systems facilitate the planning capabilities inside the adaptation logic. However, it is challenging to capture the complete reconfiguration behavior in a model. Context Feature Models used in Dynamic Software Product Lines help to specify the capabilities of a software as well as the monitored context values with the possibility to add constraints. Additionally, most adaptation logics are tailored to single use cases without the possibility for later reuse. This thesis presents an adaptation logic approach based on Dynamic Software Product Line variability models. The complete adaptation knowledge is encapsulated inside a knowledge component. This enables reuse of the complete adaptation logic. After the introduction of the approach, the adaptation logic is evaluated in a distributed computing use case

The role of Lamin B1 in the organization of the nuclear envelope and myelin regulation in development and disease

The nuclear lamina is a structural meshwork composed of intermediate filament proteins known as lamins that maintains nuclear shape and function. Perturbations of lamins lead to diseases, collectively known as laminopathies, which affect a wide variety of organ systems. One such laminopathy is autosomal dominant leukodystrophy (ADLD), a severe and fatal adult-onset demyelinating laminopathy caused by overexpression of LMNB1, one of the lamin proteins that make up the nuclear lamina. My studies aim to elucidate the role of lamin B1 in the organization of the nuclear envelope, its role in myelin regulation during oligodendrocyte maturation, and to understand how the genomic rearrangements involving LMNB1 cause ADLD. Our results suggest a novel concentric organization model of the nuclear lamina, with lamin B1 facing the inner nuclear membrane while lamins A and C together face the nucleoplasm. Lamin B1’s outward-facing localization maintains nuclear shape by restraining the lamin A/C meshwork from protruding outward. To study lamin B1’s function in mature oligodendrocytes, conditional Lmnb1 knockout mice were used to study behavioral and molecular changes in the central nervous system. Knockout mice did not exhibit any overt behavioral phenotypes or myelination defects, but a careful analysis revealed alterations in the number of myelinating oligodendrocyte populations. We conclude that while mature oligodendrocytes do not require lamin B1 for their proper function, it might be important for the regulation of oligodendrocyte cell number. Array CGH studies revealed that deletions upstream of LMNB1 can also lead to ADLD, while large duplications involving LMNB1 and a significant upstream region do not. Real-time PCR analysis demonstrate much higher LMNB1 expression in white matter than in grey matter and fibroblasts. We propose that an oligodendrocyte-specific silencer element lies upstream of LMNB1, explaining ADLD’s central nervous system exclusivity despite a constitutional LMNB1 duplication. As demyelination and white matter injuries are common in disorders affecting a wide age range – from preterm neonates to young adults and the elderly – researching pathways involved in myelination and ways to reverse it could have a significant impact to public health

Applications of realtime fMRI for non-invasive brain computer interface-decoding and neurofeedback

Non-invasive brain-computer interfaces (BCIs) seek to enable or restore brain function by using neuroimaging e.g. functional magnetic resonance imaging (fMRI), to engage brain activations without the need for explicit behavioural output or surgical implants. Brain activations are converted into output signals, for use in communication interfaces, motor prosthetics, or to directly shape brain function via a feedback loop. The aim of this thesis was to develop cognitive BCIs using realtime fMRI (rt-fMRI), with the potential for use as a communication interface, or for initiating neural plasticity to facilitate neurorehabilitation. Rt-fMRI enables brain activation to be manipulated directly to produce changes in function, such as perception. Univariate and multivariate classification approaches were used to decode brain activations produced by the deployment of covert spatial attention to simple visual stimuli. Primary and higher order visual areas were examined, as well as potential control regions. The classification platform was then developed to include the use of real-world visual stimuli, exploiting the use of category-specific visual areas, and demonstrating real-world applicability as a communications interface. Online univariate classification of spatial attention was successfully achieved, with individual classification accuracies for 4-quadrant spatial attention reaching 70%. Further, a novel implementation of m-sequences enabled the use of the timing of stimuli presentation to enhance signal characterisation. An established rt-fMRI analysis loop was then used for neurofeedback-led manipulation of category-specific visual brain regions, modulating their functioning, and, as a result, biasing visual perception during binocular rivalry. These changes were linked with functional and effective connectivity changes in trained regions, as well as in a putative top-down control region. The work presented provides proof-of-principle for non-invasive BCIs using rt-fMRI, with the potential for translation into the clinical environment. Decoding and 4 neurofeedback applied to non-invasive and implantable BCIs form an evolving continuum of options for enabling and restoring brain function

A model-based runtime environment for adapting communication systems

With increasing network sizes, mobility, and traffic, it becomes a challenging task to achieve goals such as continuously delivering a satisfying service quality. Self-adaptive approaches use feedback loops to adapt a managed resource at runtime according to changes in the execution context. Adding self-adaptive capabilities to communication systems-computer networks as well as supporting structures such as overlays or middleware-is a major research focus. However, making a communication system self-adaptive is a challenging task for communication system developers. First, the distributed nature of such systems requires the collection of monitoring information from multiple hosts and the adaptation of distributed components. Second, communication systems consist of heterogeneous components, which are, e.g., developed in different programming languages. Third, system developers typically lack knowledge about the development of self-adaptive systems. Hence, this work's overall goal is to allow system developers to focus on making a (legacy) communication system adaptive. Motivated by these observations, this thesis proposes a model-based runtime environment for adapting communication systems called REACT. In contrast to self-adaptation frameworks, which offer a standard way to build self-adaptive applications, we refer to REACT as a runtime environment, i.e., a platform that is additionally able to plan and execute adaptations based on user-specified adaptation behavior. REACT includes the support for decentralized adaptation logics and distributed systems, multiple programming languages, as well as tool support and assistance for developers. The developer support is achieved using model-based techniques for specifying the reconfiguration behavior of the adaptation logic. Also, this thesis proposes an easy-to-follow development process. As part of that, it is needed to monitor the reconfiguration behavior of the self-adaptive system. Hence, this work also presents two dashboard-based visualization approaches called CoalaViz and EnTrace for providing traceability of self-adaptive systems for system developers and administrators. This thesis follows a design science research methodology resulting in the design and implementation of the final artifacts. By that, this dissertation presents different REACT Loops, including specific ways to model and plan the adaptive behavior using satisfiability, mixed-integer linear programming, and constraint solvers. The prototypes of these approaches, including the two visualization solutions, are evaluated in multiple use cases. Therefore, this work provides an end-to-end solution for specifying the adaptive behavior, connecting a managed resource, deploying the system, as well as debugging and monitoring it