Acquiring synaesthesia: insights from training studies

Synaesthesia denotes a condition of remarkable individual differences in experience characterized by specific additional experiences in response to normal sensory input. Synaesthesia seems to (i) run in families which suggests a genetic component, (ii) is associated with marked structural and functional neural differences, and (iii) is usually reported to exist from early childhood. Hence, synaesthesia is generally regarded as a congenital phenomenon. However, most synaesthetic experiences are triggered by cultural artifacts (e.g., letters, musical sounds). Evidence exists to suggest that synaesthetic experiences are triggered by the conceptual representation of their inducer stimuli. Cases were identified for which the specific synaesthetic associations are related to prior experiences and large scale studies show that grapheme-color associations in synaesthesia are not completely random. Hence, a learning component is inherently involved in the development of specific synaesthetic associations. Researchers have hypothesized that associative learning is the critical mechanism. Recently, it has become of scientific and public interest if synaesthetic experiences may be acquired by means of associative training procedures and whether the gains of these trainings are associated with similar cognitive benefits as genuine synaesthetic experiences. In order to shed light on these issues and inform synaesthesia researchers and the general interested public alike, we provide a comprehensive literature review on developmental aspects of synaesthesia and specific training procedures in non-synaesthetes. Under the light of a clear working definition of synaesthesia, we come to the conclusion that synaesthesia can potentially be learned by the appropriate training.

A unified approach to architecture conformance checking

Architectural decisions can be interpreted as structural and behavioral constraints that must be enforced in order to guarantee overarching qualities in a system. Enforcing those constraints in a fully automated way is often challenging and not well supported by current tools. Current approaches for checking architecture conformance either lack in usability or offer poor options for adaptation. To overcome this problem we analyze the current state of practice and propose an approach based on an extensible, declarative and empirically-grounded specification language. This solution aims at reducing the overall cost of setting up and maintaining an architectural conformance monitoring environment by decoupling the conceptual representation of a user-defined rule from its technical specification prescribed by the underlying analysis tools. By using a declarative language, we are able to write tool-agnostic rules that are simple enough to be understood by untrained stakeholders and, at the same time, can be can be automatically processed by a conformance checking validator. Besides addressing the issue of cost, we also investigate opportunities for increasing the value of conformance checking results by assisting the user towards the full alignment of the implementation with respect to its architecture. In particular, we show the benefits of providing actionable results by introducing a technique which automatically selects the optimal repairing solutions by means of simulation and profit-based quantification. We perform various case studies to show how our approach can be successfully adopted to support truly diverse industrial projects. We also investigate the dynamics involved in choosing and adopting a new automated conformance checking solution within an industrial context. Our approach reduces the cost of conformance checking by avoiding the need for an explicit management of the involved validation tools. The user can define rules using a convenient high-level DSL which automatically adapts to emerging analysis requirements. Increased usability and modular customization ensure lower costs and a shorter feedback loop.

Fuzzy Web Knowledge Aggregation, Representation, and Reasoning for Online Privacy and Reputation Management

A social Semantic Web empowers its users to have access to collective Web knowledge in a simple manner, and for that reason, controlling online privacy and reputation becomes increasingly important, and must be taken seriously. This chapter presents Fuzzy Cognitive Maps (FCM) as a vehicle for Web knowledge aggregation, representation, and reasoning. With this in mind, a conceptual framework for Web knowledge aggregation, representation, and reasoning is introduced along with a use case, in which the importance of investigative searching for online privacy and reputation is highlighted. Thereby it is demonstrated how a user can establish a positive online presence.

A Conceptual Model to Combine Creativity Techniques with Fuzzy Cognitive Maps for Enhanced Knowledge Management

This chapter introduces a conceptual model to combine creativity techniques with fuzzy cognitive maps (FCMs) and aims to support knowledge management methods by improving expert knowledge acquisition and aggregation. The aim of the conceptual model is to represent acquired knowledge in a manner that is as computer-understandable as possible with the intention of developing automated reasoning in the future as part of intelligent information systems. The formal represented knowledge thus may provide businesses with intelligent information integration. To this end, we introduce and evaluate various creativity techniques with a list of attributes to define the most suitable to combine with FCMs. This proposed combination enables enhanced knowledge management through the acquisition and representation of expert knowledge with FCMs. Our evaluation indicates that the creativity technique known as mind mapping is the most suitable technique in our set. Finally, a scenario from stakeholder management demonstrates the combination of mind mapping with FCMs as an integrated system.