Temporal pattern recognition in video clips detection

Temporal representation and reasoning plays an important role in Data Mining and Knowledge Discovery, particularly, in mining and recognizing patterns with rich temporal information. Based on a formal characterization of time-series and state-sequences, this paper presents the computational technique and algorithm for matching state-based temporal patterns. As a case study of real-life applications, zone-defense pattern recognition in basketball games is specially examined as an illustrating example. Experimental results demonstrate that it provides a formal and comprehensive temporal ontology for research and applications in video events detection.

Towards Real-time Speech Emotion Recognition for Affective E-Learning

The original article is available as an open access file on the Springer website in the following link: http://link.springer.com/article/10.1007/s10639-015-9388-2

Relating sardine recruitment in the Northern Benguela to satellite-derived sea surface height using a neural network pattern recognition approach

Processes of enrichment, concentration and retention are thought to be important for the successful recruitment of small pelagic fish in upwelling areas, but are difficult to measure. In this study, a novel approach is used to examine the role of spatio-temporal oceanographic variability on recruitment success of the Northern Benguela sardine Sardinops sagax. This approach applies a neural network pattern recognition technique, called a self-organising map (SOM), to a seven-year time series of satellite-derived sea level data. The Northern Benguela is characterised by quasi-perennial upwelling of cold, nutrient-rich water and is influenced by intrusions of warm, nutrient-poor Angola Current water from the north. In this paper, these processes are categorised in terms of their influence on recruitment success through the key ocean triad mechanisms of enrichment, concentration and retention. Moderate upwelling is seen as favourable for recruitment, whereas strong upwelling, weak upwelling and Angola Current intrusion appear detrimental to recruitment success. The SOM was used to identify characteristic patterns from sea level difference data and these were interpreted with the aid of sea surface temperature data. We found that the major oceanographic processes of upwelling and Angola Current intrusion dominated these patterns, allowing them to be partitioned into those representing recruitment favourable conditions and those representing adverse conditions for recruitment. A marginally significant relationship was found between the index of sardine recruitment and the frequency of recruitment favourable conditions (r super(2) = 0.61, p = 0.068, n = 6). Because larvae are vulnerable to environmental influences for a period of at least 50 days after spawning, the SOM was then used to identify windows of persistent favourable conditions lasting longer than 50 days, termed recruitment favourable periods (RFPs). The occurrence of RFPs was compared with back-calculated spawning dates for each cohort. Finally, a comparison of RFPs with the time of spawning and the index of recruitment showed that in years where there were 50 or more days of favourable conditions following spawning, good recruitment followed (Mann-Whitney U-test: p = 0.064, n = 6). These results show the value of the SOM technique for describing spatio-temporal variability in oceanographic processes. Variability in these processes appears to be an important factor influencing recruitment in the Northern Benguela sardine, although the available data time series is currently too short to be conclusive. Nonetheless, the analysis of satellite data, using a neural network pattern-recognition approach, provides a useful framework for investigating fisheries recruitment problems.

Heterospecific mating and species recognition in the planktonic marine copepods Temora stylifera and T. longicornis

Many planktonic copepods use diffusible pheromone or hydromechanical signals to remotely detect the presence of potential mates. To determine whether these mating signals also play a role in species recognition and mate choice, we observed and video recorded (3D) mate-finding and pursuit behaviors in heterospecific and conspecific mating crosses in a pair of congeneric, partially sympatric species (Temora stylifera and T. longicornis) in the laboratory. The species appear to have asymmetrical pre-mating isolation, with T. longicornis males readily pursuing T. stylifera females to mate contact and capture, but with little mate-finding activity observed in the reverse cross. Males of T. longicornis pursuing heterospecific females executed a number of behaviors known to facilitate successful pheromone trail following and mate capture in conspecific mating, including accelerated swimming in a ‘signal-scanning’ mode to recover a lost pheromone trail, reversal of the tracking direction in cases when the male initiated tracking in the incorrect direction, and accelerated swimming speeds when in the presence of a pheromone signal but prior to locating the trail. Detailed analyses of mate-tracking behavior in T. longicornis male × T. stylifera female crosses gave no indication that males were aware they were pursuing heterospecific females prior to mate contact, indicating that diffusible pheromone and hydromechanical signals are not used, either singly or in combination, for species recognition in this mating pair. Heterospecific mating attempts among sympatric, congeneric copepods may commonly proceed to mate capture, and incur fitness costs to either or both mating partners.

Speech Recognition with unknown partial feature corruption - a review of the union model

This paper provides a summary of our studies on robust speech recognition based on a new statistical approach – the probabilistic union model. We consider speech recognition given that part of the acoustic features may be corrupted by noise. The union model is a method for basing the recognition on the clean part of the features, thereby reducing the effect of the noise on recognition. To this end, the union model is similar to the missing feature method. However, the two methods achieve this end through different routes. The missing feature method usually requires the identity of the noisy data for noise removal, while the union model combines the local features based on the union of random events, to reduce the dependence of the model on information about the noise. We previously investigated the applications of the union model to speech recognition involving unknown partial corruption in frequency band, in time duration, and in feature streams. Additionally, a combination of the union model with conventional noise-reduction techniques was studied, as a means of dealing with a mixture of known or trainable noise and unknown unexpected noise. In this paper, a unified review, in the context of dealing with unknown partial feature corruption, is provided into each of these applications, giving the appropriate theory and implementation algorithms, along with an experimental evaluation.