78,063 research outputs found
When and Where: Predicting Human Movements Based on Social Spatial-Temporal Events
Predicting both the time and the location of human movements is valuable but
challenging for a variety of applications. To address this problem, we propose
an approach considering both the periodicity and the sociality of human
movements. We first define a new concept, Social Spatial-Temporal Event (SSTE),
to represent social interactions among people. For the time prediction, we
characterise the temporal dynamics of SSTEs with an ARMA (AutoRegressive Moving
Average) model. To dynamically capture the SSTE kinetics, we propose a Kalman
Filter based learning algorithm to learn and incrementally update the ARMA
model as a new observation becomes available. For the location prediction, we
propose a ranking model where the periodicity and the sociality of human
movements are simultaneously taken into consideration for improving the
prediction accuracy. Extensive experiments conducted on real data sets validate
our proposed approach
Is Segregation Robust?
This paper studies the question of how well we understand segregation. The point of departure is Schelling’s spatial proximity model in one dimension. By introducing noise I show that segregation emerges as the long run prediction of neighborhood evolution, both when residents have Schelling-type threshold preferences and strict preferences for diversity. Analytical result are complemented with numerical simulations which show that within a reasonable time frame full segregation does not occur. When residents have a preference for diversity, I show that a natural perturbation away from the diversity monomorphism dramatically alters the long run prediction: integration is the unique long run prediction, even in the absence of noise.segregation; Markov Process; Stochastic Stability; simulations
Probabilistic Human Mobility Model in Indoor Environment
Understanding human mobility is important for the development of intelligent
mobile service robots as it can provide prior knowledge and predictions of
human distribution for robot-assisted activities. In this paper, we propose a
probabilistic method to model human motion behaviors which is determined by
both internal and external factors in an indoor environment. While the internal
factors are represented by the individual preferences, aims and interests, the
external factors are indicated by the stimulation of the environment. We model
the randomness of human macro-level movement, e.g., the probability of visiting
a specific place and staying time, under the Bayesian framework, considering
the influence of both internal and external variables. We use two case studies
in a shopping mall and in a college student dorm building to show the
effectiveness of our proposed probabilistic human mobility model. Real
surveillance camera data are used to validate the proposed model together with
survey data in the case study of student dorm.Comment: 8 pages, 9 figures, International Joint Conference on Neural Networks
(IJCNN) 201
Change blindness: eradication of gestalt strategies
Arrays of eight, texture-defined rectangles were used as stimuli in a one-shot change blindness (CB) task where there was a 50% chance that one rectangle would change orientation between two successive presentations separated by an interval. CB was eliminated by cueing the target rectangle in the first stimulus, reduced by cueing in the interval and unaffected by cueing in the second presentation. This supports the idea that a representation was formed that persisted through the interval before being 'overwritten' by the second presentation (Landman et al, 2003 Vision Research 43149–164]. Another possibility is that participants used some kind of grouping or Gestalt strategy. To test this we changed the spatial position of the rectangles in the second presentation by shifting them along imaginary spokes (by ±1 degree) emanating from the central fixation point. There was no significant difference seen in performance between this and the standard task [F(1,4)=2.565, p=0.185]. This may suggest two things: (i) Gestalt grouping is not used as a strategy in these tasks, and (ii) it gives further weight to the argument that objects may be stored and retrieved from a pre-attentional store during this task
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