New media art, participation, social engagement and public funding

This article investigates the current condition of new media art in Britain, examining how cuts to arts funding have affected the art form's infrastructure and capacity for survival and growth. It considers media art in relation to other contemporary art practices, particularly in relation to its inherent capacity for enhanced and sustained user participation, and asks why it is that, though government agendas favour participatory art as ‘socially useful', media art appears to have been hit harder than other art forms. The article puts forward four reasons that could explain this paradox, and argues the importance of the survival of new media art, not as isolated practices invited to exist within mainstream contexts, but as a distinct art form

Morphological Computing in Cognitive Systems, Connecting Data to Intelligent Agency

According to the currently dominant view, cognitive science is a study of mind and intelligence focused on computational models of knowledge in humans. It is described in terms of symbol manipulation over formal language. This approach is connected with a variety of unsolvable problems, as pointed out by Thagard. In this paper, I argue that the main reason for the inadequacy of the traditional view of cognition is that it detaches the body of a human as the cognizing agent from the higher-level abstract knowledge generation. It neglects the dynamical aspects of cognitive processes, emotions, consciousness, and social aspects of cognition. It is also uninterested in other cognizing agents such as other living beings or intelligent machines. Contrary to the traditional computationalism in cognitive science, the morphological computation approach offers a framework that connects low-level with high-level approaches to cognition, capable of meeting challenges listed by Thagard. To establish this connection, morphological computation generalizes the idea of computation from symbol manipulation to natural/physical computation and the idea of cognition from the exclusively human capacity to the capacity of all goal-directed adaptive self-reflective systems, living organisms as well as robots. Cognition is modeled as a layered process, where at the lowest level, systems acquire data from the environment, which in combination with the already stored data in the morphology of an agent, presents the basis for further structuring and self-organization of data into information and knowledge

ConDRust: Scalable Deterministic Concurrency from Verifiable Rust Programs

SAT/SMT-solvers and model checkers automate formal verification of sequential programs. Formal reasoning about scalable concurrent programs is still manual and requires expert knowledge. But scalability is a fundamental requirement of current and future programs. Sequential imperative programs compose statements, function/method calls and control flow constructs. Concurrent programming models provide constructs for concurrent composition. Concurrency abstractions such as threads and synchronization primitives such as locks compose the individual parts of a concurrent program that are meant to execute in parallel. We propose to rather compose the individual parts again using sequential composition and compile this sequential composition into a concurrent one. The developer can use existing tools to formally verify the sequential program while the translated concurrent program provides the dearly requested scalability. Following this insight, we present ConDRust, a new programming model and compiler for Rust programs. The ConDRust compiler translates sequential composition into a concurrent composition based on threads and message-passing channels. During compilation, the compiler preserves the semantics of the sequential program along with much desired properties such as determinism. Our evaluation shows that our ConDRust compiler generates concurrent deterministic code that can outperform even non-deterministic programs by up to a factor of three for irregular algorithms that are particularly hard to parallelize