Myoelectronic signal-based methodology for the analysis of handwritten signatures

With the overall aim of improving the synthesis of handwritten signatures, we have studied how muscle activation depends on handwriting style for both text and flourish. Surface electromyographic (EMG) signals from a set of twelve arm and trunk muscles were recorded in synchronization with handwriting produced on a digital Tablet. Correlations between these EMG signals and handwritten trajectory signals were analyzed so as to define the sequence of muscles activated during the different parts of the signature. Our results establish a correlation between the speed of the movement, stroke size, handwriting style and muscle activation. Muscle activity appeared to be clustered as a function of movement speed and handwriting style, a finding which may be used for filter design in a signature synthesizer.     

Do handwritten words magnify lexical effects in visual word recognition?

An examination of how the word recognition system is able to process handwritten words is fundamental to formulate a comprehensive model of visual word recognition. Previous research has revealed that the magnitude of lexical effects (e.g., the word-frequency effect) is greater with handwritten words than with printed words. In the present lexical decision experiments, we examined whether the quality of handwritten words moderates the recruitment of top-down feedback, as reflected in word-frequency effects. Results showed a reading cost for difficult-to-read and easy-to-read handwritten words relative to printed words. But the critical finding was that difficult-to-read handwritten words, but not easy-to-read handwritten words, showed a greater word-frequency effect than printed words. Therefore, the inherent physical variability of handwritten words does not necessarily boost the magnitude of lexical effects.     

Fuzzy system for intelligent word recognition using a regular grammar

We present a new approach for off-line intelligent word recognition based on a fuzzy classification model. First, we segment a word into its single characters, and label each pixel as vertical or as horizontal so that we can group all the pixels into vertical or horizontal strokes. Then, we use dynamic zoning to obtain the locations of the connections between the vertical strokes – which are the main strokes – and the horizontal ones. These features let us construct the representative string of a character using a regular grammar and, subsequently, use a Deterministic Finite Automaton to check them out. To accomplish the recognition, we use a Fuzzy Lattice Reasoning classifier. The combination of the representative strings and the fuzzy classifier provides promising performance rates.     

关于定州汉墓竹简《论语》的几个问题

1973年定州汉墓出土竹简《论语》是现今所见最早的《论语》抄本,通过统计对照简文避讳用字,我们认为定州汉墓竹简《论语》当属汉高祖刘邦时期抄本。因为《古论》是汉武帝时期才出现,而《鲁论》与《齐论》的产生年代又晚于《古论》,所以竹简《论语》已经超出了传统已知汉代三论的范围,属汉初今文隶书《论语》。     

Signature size signals sociable dominance and narcissism

Handwritten signatures have been traditionally associated with personality traits. In this study, we examined the relationship of signature size with intrasexual competition, aggressive and sociable dominance, narcissism, and self-esteem in a sample of Uruguayan university students (N = 340). The study went methodologically beyond current research by examining three different operationalizations of signature size, and by controlling for potential confounders: number of characters in the printed name, average character area in the printed name—a proxy for overall writing size—, and signature style. After controlling for these potential confounders and demographic variables, our results showed a significant link between signature size and sociable dominance, both for males and females, while narcissism was only in females significantly associated with signature size.     

Identifying the situational triggers underlying avoidance of communication situations and individual differences therein

People are actively involved in the selection and avoidance of the situations they face during everyday life. Moreover, such selection/avoidance behavior is subject to sizeable individual differences. Yet, to a large extent this phenomenon has been underinvestigated, and a full understanding of selection/avoidance remains lacking. In the present paper, we take a first step to a more in-depth understanding of situation avoidance, which is conceptualized in terms of individual profiles or signatures across situations. Two key objectives with regard to those signatures are being addressed, that is: (a) identifying the critical situational triggers that elicit avoidance behavior, and (b) identifying the most important individual differences in the link between these situational triggers and avoidance, along with their underlying process dynamics. To achieve these objectives, we performed an empirical study on avoidance of communication situations. This study revealed a set of person types that meaningfully differ in sensitivity to a few key situational features. These person types further appeared to differ from one another on several dispositional cognitive/affective forecast variables.