Graph-based relational reasoning in a latent space for skeleton-based action recognition

Author(s):  
Wenwen Ding ◽  
GuangHui Zhou ◽  
ChongYang Ding ◽  
Guang Li ◽  
Kai Liu
2019 ◽  
Vol 42 ◽  
Author(s):  
Daniel J. Povinelli ◽  
Gabrielle C. Glorioso ◽  
Shannon L. Kuznar ◽  
Mateja Pavlic

Abstract Hoerl and McCormack demonstrate that although animals possess a sophisticated temporal updating system, there is no evidence that they also possess a temporal reasoning system. This important case study is directly related to the broader claim that although animals are manifestly capable of first-order (perceptually-based) relational reasoning, they lack the capacity for higher-order, role-based relational reasoning. We argue this distinction applies to all domains of cognition.


2015 ◽  
Vol 36 (4) ◽  
pp. 228-236 ◽  
Author(s):  
Janko Međedović ◽  
Boban Petrović

Abstract. Machiavellianism, narcissism, and psychopathy are personality traits understood to be dispositions toward amoral and antisocial behavior. Recent research has suggested that sadism should also be added to this set of traits. In the present study, we tested a hypothesis proposing that these four traits are expressions of one superordinate construct: The Dark Tetrad. Exploration of the latent space of four “dark” traits suggested that the singular second-order factor which represents the Dark Tetrad can be extracted. Analysis has shown that Dark Tetrad traits can be located in the space of basic personality traits, especially on the negative pole of the Honesty-Humility, Agreeableness, Conscientiousness, and Emotionality dimensions. We conclude that sadism behaves in a similar manner as the other dark traits, but it cannot be reduced to them. The results support the concept of “Dark Tetrad.”


2020 ◽  
Vol 36 (2) ◽  
pp. 296-302 ◽  
Author(s):  
Luke J. Hearne ◽  
Damian P. Birney ◽  
Luca Cocchi ◽  
Jason B. Mattingley

Abstract. The Latin Square Task (LST) is a relational reasoning paradigm developed by Birney, Halford, and Andrews (2006) . Previous work has shown that the LST elicits typical reasoning complexity effects, such that increases in complexity are associated with decrements in task accuracy and increases in response times. Here we modified the LST for use in functional brain imaging experiments, in which presentation durations must be strictly controlled, and assessed its validity and reliability. Modifications included presenting the components within each trial serially, such that the reasoning and response periods were separated. In addition, the inspection time for each LST problem was constrained to five seconds. We replicated previous findings of higher error rates and slower response times with increasing relational complexity and observed relatively large effect sizes (η2p > 0.70, r > .50). Moreover, measures of internal consistency and test-retest reliability confirmed the stability of the LST within and across separate testing sessions. Interestingly, we found that limiting the inspection time for individual problems in the LST had little effect on accuracy relative to the unconstrained times used in previous work, a finding that is important for future brain imaging experiments aimed at investigating the neural correlates of relational reasoning.


Methodology ◽  
2006 ◽  
Vol 2 (1) ◽  
pp. 24-33 ◽  
Author(s):  
Susan Shortreed ◽  
Mark S. Handcock ◽  
Peter Hoff

Recent advances in latent space and related random effects models hold much promise for representing network data. The inherent dependency between ties in a network makes modeling data of this type difficult. In this article we consider a recently developed latent space model that is particularly appropriate for the visualization of networks. We suggest a new estimator of the latent positions and perform two network analyses, comparing four alternative estimators. We demonstrate a method of checking the validity of the positional estimates. These estimators are implemented via a package in the freeware statistical language R. The package allows researchers to efficiently fit the latent space model to data and to visualize the results.


2017 ◽  
Vol 31 (2) ◽  
pp. 200-208 ◽  
Author(s):  
Emiliano Brunamonti ◽  
Floriana Costanzo ◽  
Anna Mammì ◽  
Cristina Rufini ◽  
Diletta Veneziani ◽  
...  

2013 ◽  
Vol 18 (2-3) ◽  
pp. 49-60 ◽  
Author(s):  
Damian Dudzńiski ◽  
Tomasz Kryjak ◽  
Zbigniew Mikrut

Abstract In this paper a human action recognition algorithm, which uses background generation with shadow elimination, silhouette description based on simple geometrical features and a finite state machine for recognizing particular actions is described. The performed tests indicate that this approach obtains a 81 % correct recognition rate allowing real-time image processing of a 360 X 288 video stream.


2018 ◽  
Vol 6 (10) ◽  
pp. 323-328
Author(s):  
K.Kiruba . ◽  
D. Shiloah Elizabeth ◽  
C Sunil Retmin Raj

2019 ◽  
Author(s):  
Giacomo De Rossi ◽  
◽  
Nicola Piccinelli ◽  
Francesco Setti ◽  
Riccardo Muradore ◽  
...  

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