Many psychological studies have shown that human-generated sequences are hardly ever random in the strict mathematical sense. However, what remains an open question is the degree to which this (in)ability varies betwe...
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Many psychological studies have shown that human-generated sequences are hardly ever random in the strict mathematical sense. However, what remains an open question is the degree to which this (in)ability varies between people and is affected by contextual factors. Herein, we investigated this problem. In two studies, we used a modern, robust measure of randomness based on algorithmic information theory to assess human-generated series. In Study 1 (N=183), in a factorial design with task description as a between-subjects variable, we tested the effects of context and mental fatigue on human-generated randomness. In Study 2 (N=266), in online research, in experimental design, we further investigated the effect of mental fatigue on the randomness of human-generated series and the relationship between the need for cognition (NFC) and the ability to produce random-like series. Results of Study 1 show that the activation of the ability to produce random-like series depends on the relevance of the contextual cues (chi 2(2)=7.9828,p=.0192), whether they activate known representations of a random series generator and consequently help to avoid the production of trivial sequences. Our findings from both studies on the effect of mental fatigue (Study 1 - t(47,529.5568)=-18.62,p<.001;Study 2 - F(edf=3.587,***=3.587)=11.863,p<.0001) and cognitive motivation (t(180)=2.66,p=.009) demonstrate that regardless of the context or task's novelty people quickly lose interest in the random series generation. Therefore, their performance decreases over time. However, people high in the NFC can maintain the cognitive motivation for a longer period and consequently on average generate more random series. In general, our results suggest that when contextual cues and intrinsic constraints are in optimal interaction people can temporarily escape the structured and trivial patterns and produce more random-like sequences.
As computer systems scale in the number of processors, scalable data structures with good parallel performance become increasingly important. Lock-free data structures promise such improved parallel performance at the...
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ISBN:
(纸本)9781479910090
As computer systems scale in the number of processors, scalable data structures with good parallel performance become increasingly important. Lock-free data structures promise such improved parallel performance at the expense of higher algorithmic complexity and higher sequential execution time overhead. All lock-free data structures are based on simple atomic operations that, though supported by modern processors, are expensive in execution time. We present a lock-free data structure, ELB-trees, which under certain assumptions can be used as multimaps as well as priority queues. Specifically it cannot store duplicate key-value pairs, and it is not linearizable. Compared to existing data structures, ELB-trees require fewer atomic operations leading to improved performance. We measure the parallel performance of ELB-trees using a set of benchmarks and observe that ELB-trees are up to almost 30 times faster than library multimap implementations.
This paper discusses the implementation details of a child friendly, good quality, English text-to-speech (TTS) system that is phoneme-based, concatenative, easy to set up and use with little memory. Direct waveform c...
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This paper discusses the implementation details of a child friendly, good quality, English text-to-speech (TTS) system that is phoneme-based, concatenative, easy to set up and use with little memory. Direct waveform concatenation and linear prediction coding (LPC) are used. Most existing TTS systems are unit-selection based, which use standard speech databases available in neutral adult voices. Here reduced memory is achieved by the concatenation of phonemes and by replacing phonetic wave files with their LPC coefficients. Linguistic analysis was used to reduce the algorithmic complexity instead of signal processing techniques. Sufficient degree of customization and generalization catering to the needs of the child user had been included through the provision for vocabulary and voice selection to suit the requisites of the child. Prosody had also been incorporated. This inexpensive TTS system was implemented in MATLAB, with the synthesis presented by means of a graphical user interface (GUI), thus making it child friendly. This can be used not only as an interesting language learning aid for the normal child but it also serves as a speech aid to the vocally disabled child. The quality of the synthesized speech was evaluated using the mean opinion score (MOS). Copyright (C) 2008 A. Jacob and P. Mythili.
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