An integrated approach for service selection using non-functional properties and composition context

In the maturing world of service oriented computing and Web services, we find ourselves in a position where numerous services are available, all of which address a specific need. Selecting the best such service based on the service context and a user’s current need becomes an important aspect. Services can be evaluated based on functional and non-functional criteria: the former represent the operation that the service provides, the latter criteria that differentiate functionally equal services. This chapter presents three closely related items addressing the problem of differentiating functionally equal services to find the most appropriate one in any given situation: (1) a generic and extensible model for non-functional properties, (2) a method for ranking services, and (3) an algorithm for selecting services that are part of larger execution chains. The method is evaluated, and the needs are exemplified with some motivating examples

Adaptive learning is structure learning in time

People use information flexibly. They often combine multiple sources of relevant information over time in order to inform decisions with little or no interference from intervening irrelevant sources. They adjust the degree to which they use new information over time rationally in accordance with environmental statistics and their own uncertainty. They can even use information gained in one situation to solve a problem in a very different one. Learning flexibly rests on the ability to infer the context at a given time, and therefore knowing which pieces of information to combine and which to separate. We review the psychological and neural mechanisms behind adaptive learning and structure learning to outline how people pool together relevant information, demarcate contexts, prevent interference between information collected in different contexts, and transfer information from one context to another. By examining all of these processes through the lens of optimal inference we bridge concepts from multiple fields to provide a unified multi-system view of how the brain exploits structure in time to optimize learning

Personality, internet addiction, and other technological addictions: an update of the research literature

There has been a significant shift from the view that addictions are disorders involving compulsivedrug usage to a view that non-substance related behaviors may now be considered addictions. Thereis evidence to suggest that people are showing signs of addiction to non-substance-related behaviors.Research into technological addictions, such as internet addiction, smartphone addiction and socialnetworking addiction has exponentially increased over the last decade. Understanding how technologicaladdictions relate to personality and key individual differences is important. This chapter providesrenewed empirical and conceptual insights into technological addictions and how they may be relatedto different personality types and key individual differences. The complex nature of personality andtechnological addictions is discussed together with areas for future research