202,704 research outputs found
Musical Program Auralisation: Empirical Studies
Program auralisation aims to communicate information about program state, data, and behaviour using audio. We have argued that music offers many advantages as a communication medium [1]. The CAITLIN system [4, 16, 17, 18] was constructed to provide auralisations within a formal structured musical framework. Pilot studies [4, 16] showed that programmers could infer program structure from auralisations alone. A study was conducted using twenty-two novice programmers to assess a) whether novices could understand the musical auralisations and b) whether the musical experience and knowledge of subjects affected their performance. The results show that novices could interpret the auralisations (with accuracy varying across different levels of abstraction) and that musical knowledge had no significant effect on performance. A second experiment was conducted with another twenty-two novice programmers to study the effects of musical program auralisation on debugging tasks. The experiment aimed to determine whether auralisations would lead to higher bug detection rates. The results indicate that, in certain circumstances, musical auralisations can be used to help locate bugs in programs and that musical skill does not affect the ability to make use of the auralisations. In addition, it the experiment showed that subjective workload increased when the musical auralisations were used
Cognitive processes in categorical and associative priming: a diffusion model analysis
Cognitive processes and mechanisms underlying different forms of priming were investigated using a diffusion model approach. In a series of 6 experiments, effects of prime-target associations and of a semantic and affective categorical match of prime and target were analyzed for different tasks. Significant associative and categorical priming effects were found in standard analyses of response times (RTs) and error frequencies. Results of diffusion model analyses revealed that priming effects of associated primes were mapped on the drift rate parameter (v), while priming effects of a categorical match on a task-relevant dimension were mapped on the extradecisional parameters (t(0) and d). These results support a spreading activation account of associative priming and an explanation of categorical priming in terms of response competition. Implications for the interpretation of priming effects and the use of priming paradigms in cognitive psychology and social cognition are discussed
POWERPLAY: Training an Increasingly General Problem Solver by Continually Searching for the Simplest Still Unsolvable Problem
Most of computer science focuses on automatically solving given computational
problems. I focus on automatically inventing or discovering problems in a way
inspired by the playful behavior of animals and humans, to train a more and
more general problem solver from scratch in an unsupervised fashion. Consider
the infinite set of all computable descriptions of tasks with possibly
computable solutions. The novel algorithmic framework POWERPLAY (2011)
continually searches the space of possible pairs of new tasks and modifications
of the current problem solver, until it finds a more powerful problem solver
that provably solves all previously learned tasks plus the new one, while the
unmodified predecessor does not. Wow-effects are achieved by continually making
previously learned skills more efficient such that they require less time and
space. New skills may (partially) re-use previously learned skills. POWERPLAY's
search orders candidate pairs of tasks and solver modifications by their
conditional computational (time & space) complexity, given the stored
experience so far. The new task and its corresponding task-solving skill are
those first found and validated. The computational costs of validating new
tasks need not grow with task repertoire size. POWERPLAY's ongoing search for
novelty keeps breaking the generalization abilities of its present solver. This
is related to Goedel's sequence of increasingly powerful formal theories based
on adding formerly unprovable statements to the axioms without affecting
previously provable theorems. The continually increasing repertoire of problem
solving procedures can be exploited by a parallel search for solutions to
additional externally posed tasks. POWERPLAY may be viewed as a greedy but
practical implementation of basic principles of creativity. A first
experimental analysis can be found in separate papers [53,54].Comment: 21 pages, additional connections to previous work, references to
first experiments with POWERPLA
i2MapReduce: Incremental MapReduce for Mining Evolving Big Data
As new data and updates are constantly arriving, the results of data mining
applications become stale and obsolete over time. Incremental processing is a
promising approach to refreshing mining results. It utilizes previously saved
states to avoid the expense of re-computation from scratch.
In this paper, we propose i2MapReduce, a novel incremental processing
extension to MapReduce, the most widely used framework for mining big data.
Compared with the state-of-the-art work on Incoop, i2MapReduce (i) performs
key-value pair level incremental processing rather than task level
re-computation, (ii) supports not only one-step computation but also more
sophisticated iterative computation, which is widely used in data mining
applications, and (iii) incorporates a set of novel techniques to reduce I/O
overhead for accessing preserved fine-grain computation states. We evaluate
i2MapReduce using a one-step algorithm and three iterative algorithms with
diverse computation characteristics. Experimental results on Amazon EC2 show
significant performance improvements of i2MapReduce compared to both plain and
iterative MapReduce performing re-computation
Learning Dimensions: Lessons from Field Studies
In this paper, we describe work to investigate the creation of engaging programming learning experiences. Background research informed the design of four fieldwork studies involving a range of age groups to explore how programming tasks could best be framed to motivate learners. Our empirical findings from these four studies, described here, contributed to the design of a set of programming "Learning Dimensions" (LDs). The LDs provide educators with insights to support key design decisions for the creation of engaging programming learning experiences. This paper describes the background to the identification of these LDs and how they could address the design and delivery of highly engaging programming learning tasks. A web application has been authored to support educators in the application of the LDs to their lesson design
Shared Arrangements: practical inter-query sharing for streaming dataflows
Current systems for data-parallel, incremental processing and view
maintenance over high-rate streams isolate the execution of independent
queries. This creates unwanted redundancy and overhead in the presence of
concurrent incrementally maintained queries: each query must independently
maintain the same indexed state over the same input streams, and new queries
must build this state from scratch before they can begin to emit their first
results. This paper introduces shared arrangements: indexed views of maintained
state that allow concurrent queries to reuse the same in-memory state without
compromising data-parallel performance and scaling. We implement shared
arrangements in a modern stream processor and show order-of-magnitude
improvements in query response time and resource consumption for interactive
queries against high-throughput streams, while also significantly improving
performance in other domains including business analytics, graph processing,
and program analysis
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