Taskflow-oriented programming merges concepts from structured programming, hardware description, and mark-up languages. A mark-up language such as XML supports a well-defined schema that can capture the decomposition of a program into a hierarchy of tasks. A hardware description language such as VHDL relies on well-defined and explicit input/output port definitions to create, at any level of hi...

{ Taskkow-oriented programming merges concepts from structured programming, hardware description, and markup languages. A markup language such as XML supports a well-deened schema that can capture the decomposition of a program into a hierarchy of tasks. A hardware description language such as VHDL relies on well-deened and explicit input/output port deenitions to create, at any level of hierar...

Recurrent neural network architectures combining with attention mechanism, or neural attention model, have shown promising performance recently for the tasks including speech recognition, image caption generation, visual question answering and machine translation. In this paper, neural attention model is applied on two sequence labeling tasks, dialogue act detection and key term extraction. In ...

Cloud computing technology offers a new way to develop workflow system. Migrating workflow system based on cloud computing paradigm (MWfSCC) can obtain high system performance and service quality in cloud computing environment, where an agent (called CMI) is deployed to fulfill several tasks by migrating to other computers (called work machines). In general, task execution involves several inpu...

ISSN 2277 – 5064 | © 2012 Bonfring Abstract--Cloud Computing is providing computing as a service rather than product such as shared resources, software information, etc...Cloud computing can be used for dispatching user tasks or jobs to the available system resource like storage and software. Scheduling algorithm is used for dispatching user tasks. In Job scheduling using fuzzy neural network a...

The present study investigates the impact of meaningful input on L2 learners’ vocabulary use and their fluency in oral performance (immediate repeat tasks), as well whether effects are mediated by prior knowledge working memory. Ninety university students learning English a foreign language were randomly assigned to one three groups: (N = 29), repetition 32), no-input (i.e., baseline group) 29)...

Inhibition of return (IOR) most often describes the finding of increased response times to cued as compared to uncued targets in the standard covert orienting paradigm. A perennial question in the IOR literature centers on whether the effect of IOR is on motoric/decision-making processes (output-based IOR), attentional/perceptual processes (input-based IOR), or both. Recent data converge on the...

We consider the learning of algorithmic tasks by mere observation of input-output pairs. Rather than studying this as a black-box discrete regression problem with no assumption whatsoever on the input-output mapping, we concentrate on tasks that are amenable to the principle of divide and conquer, and study what are its implications in terms of learning. This principle creates a powerful induct...

How should one apply deep learning to tasks such as morphological reinflection, which stochastically edit one string to get another? A recent approach to such sequence-to-sequence tasks is to compress the input string into a vector that is then used to generate the output string, using recurrent neural networks. In contrast, we propose to keep the traditional architecture, which uses a finite-s...

This paper presents a novel approach for generating Context-Free Grammars (CFGs) from small sets of input strings (a single input string in some cases). Our approach is to compile this task into a classical planning problem whose solutions are sequences of actions that build and validate a CFG compliant with the input strings. In addition, we show that our compilation is suitable for implementi...