Programming with Multiple Paradigms in Lua

Ierusalimschy, Roberto

doi:10.1007/978-3-642-11999-6_1

Cited by 29 publications

(33 citation statements)

References 14 publications

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“…The feedback consisted of a superposition of the virtual blade on the transparent virtual target blade, which was moving along the displayed target trajectory (Figure 1). The feedback was programmed in Lua (Ierusalimschy, 2013; http://www.lua.org).…”

Section: Concurrent Visual Feedbackmentioning

confidence: 99%

Terminal Feedback Outperforms Concurrent Visual, Auditory, and Haptic Feedback in Learning a Complex Rowing-Type Task

Sigrist

Rauter

Riener

et al. 2013

Journal of Motor Behavior

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Augmented feedback, provided by coaches or displays, is a well-established strategy to accelerate motor learning. Frequent terminal feedback and concurrent feedback have been shown to be detrimental for simple motor task learning but supportive for complex motor task learning. However, conclusions on optimal feedback strategies have been mainly drawn from studies on artificial laboratory tasks with visual feedback only. Therefore, the authors compared the effectiveness of learning a complex, 3-dimensional rowing-type task with either concurrent visual, auditory, or haptic feedback to self-controlled terminal visual feedback. Results revealed that terminal visual feedback was most effective because it emphasized the internalization of task-relevant aspects. In contrast, concurrent feedback fostered the correction of task-irrelevant errors, which hindered learning. The concurrent visual and haptic feedback group performed much better during training with the feedback than in nonfeedback trials. Auditory feedback based on sonification of the movement error was not practical for training the 3-dimensional movement for most participants. Concurrent multimodal feedback in combination with terminal feedback may be most effective, especially if the feedback strategy is adapted to individual preferences and skill level. ABSTRACT. Augmented feedback, provided by coaches or displays, is a well-established strategy to accelerate motor learning. Frequent terminal feedback and concurrent feedback have been shown to be detrimental for simple motor task learning but supportive for complex motor task learning. However, conclusions on optimal feedback strategies have been mainly drawn from studies on artificial laboratory tasks with visual feedback only. Therefore, the authors compared the effectiveness of learning a complex, 3-dimensional rowing-type task with either concurrent visual, auditory, or haptic feedback to self-controlled terminal visual feedback. Results revealed that terminal visual feedback was most effective because it emphasized the internalization of task-relevant aspects. In contrast, concurrent feedback fostered the correction of taskirrelevant errors, which hindered learning. The concurrent visual and haptic feedback group performed much better during training with the feedback than in nonfeedback trials. Auditory feedback based on sonification of the movement error was not practical for training the 3-dimensional movement for most participants. Concurrent multimodal feedback in combination with terminal feedback may be most effective, especially if the feedback strategy is adapted to individual preferences and skill level.

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Section: Concurrent Visual Feedbackmentioning

confidence: 99%

Terminal Feedback Outperforms Concurrent Visual, Auditory, and Haptic Feedback in Learning a Complex Rowing-Type Task

Sigrist

Rauter

Riener

et al. 2013

Journal of Motor Behavior

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“…In software application, the script -is a program that automates certain task that user would perform manually using interface without script [4,5].…”

Section: Script As a Core Of Software Flexibilitymentioning

confidence: 99%

Flexible task-driven system for image processing

Belotserkovsky

Nedzved

Lukashevich

2013

The International Conference on Digital Technologies 2013

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Conceptual schemes for process management of image processing system is proposed here so to generate a Graphic User Interface, according to specific needs of certain users and developers. Schemes allow modifications and buildingup the functionality since it based on a script-kernel and intelligent support by collaborating approach, which gives ability to use objective and reliable algorithms to solve applied tasks.

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“…Like QEMU, TCOSim works by translating each source ISA instruction into a sequence of tiny operations, but in TCOSim this sequence consists of a moderately deep series of nested function calls in the Lua scripting language [6,7], rather than a sequence of statements, as is typical of a C-language simulator, or TCG declarations, as in QEMU. The stack of function calls for each instruction has a close analogy to the multiple (post-decode) stages of a classic RISC hardware pipeline: the first function call in that stack reads a register value or constant, the next reads the second register operand if necessary, the third may perform an ALU operation, etc.…”

Section: Pipelined Processor Analogymentioning

confidence: 99%

Fast functional simulation with a dynamic language

Steele¹,

Bonn²

2012

2012 IEEE Conference on High Performance Extreme Computing

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Simulation of large computational systems-on-a-chip (SoCs) is increasing challenging as the number and complexity of components is scaled up. With the ubiquity of programmable components in computational SoCs, fast functional instructionset simulation (ISS) is increasingly important. Much ISS has been done with straightforward functional models of a non-pipelined fetch-decode-execute iteration written in a low-to-mid-level Cfamily static language, delivering mid-level efficiency. Some ISS programs, such as QEMU, perform dynamic binary translation to allow software emulation to reach more usable speeds. This relatively complex methodology has not been widely adopted for system modeling.We demonstrate a fresh approach to ISS that achieves performance comparable to a fast dynamic binary translator by exploiting recent advances in just-in-time (JIT) compilers for dynamic languages, such as JavaScript and Lua, together with a specific programming idiom inspired by pipelined processor design. We believe that this approach is relatively accessible to system designers familiar with C-family functional simulator coding styles, and may be generally useful for fast modeling of complex SoC components.

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Programming with Multiple Paradigms in Lua

Cited by 29 publications

References 14 publications

Terminal Feedback Outperforms Concurrent Visual, Auditory, and Haptic Feedback in Learning a Complex Rowing-Type Task

Terminal Feedback Outperforms Concurrent Visual, Auditory, and Haptic Feedback in Learning a Complex Rowing-Type Task

Flexible task-driven system for image processing

Fast functional simulation with a dynamic language

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