VideoCapsuleNet: A Simplified Network for Action Detection

The recent advances in Deep Convolutional Neural Networks (DCNNs) have shown extremely good results for video human action classification, however, action detection is still a challenging problem. The current action detection approaches follow a complex pipeline which involves multiple tasks such as tube proposals, optical flow, and tube classification. In this work, we present a more elegant solution for action detection based on the recently developed capsule network. We propose a 3D capsule network for videos, called VideoCapsuleNet: a unified network for action detection which can jointly perform pixel-wise action segmentation along with action classification. The proposed network is a generalization of capsule network from 2D to 3D, which takes a sequence of video frames as input. The 3D generalization drastically increases the number of capsules in the network, making capsule routing computationally expensive. We introduce capsule-pooling in the convolutional capsule layer to address this issue which makes the voting algorithm tractable. The routing-by-agreement in the network inherently models the action representations and various action characteristics are captured by the predicted capsules. This inspired us to utilize the capsules for action localization and the class-specific capsules predicted by the network are used to determine a pixel-wise localization of actions. The localization is further improved by parameterized skip connections with the convolutional capsule layers and the network is trained end-to-end with a classification as well as localization loss. The proposed network achieves sate-of-the-art performance on multiple action detection datasets including UCF-Sports, J-HMDB, and UCF-101 (24 classes) with an impressive ~20% improvement on UCF-101 and ~15% improvement on J-HMDB in terms of v-mAP scores

Analysis of Demersal Fish Schooling Distribution in Tarakan Waters North Borneo by Using Hidroacoustic Method

This research is aimed to determine the distribution of demersal fish schooling and the relation between demersal fish schooling and temperature, salinity and depth of water by using hydroacoustic method. The research was held in August 2014 at the Research Institute of Marine Fisheries Laboratory of Muara Baru, North Jakarta. This research used hydroacoustic method with acoustic descriptor techniques. The amount of fish schooling was obtained by digitization and integration, the values of integration would have generated the position and depth of the waters. Demersal fish schooling which found in waters of Tarakan were comprising of 19 schooling. The dominant fish schooling occured at temperature of 270 C to 280 C and tended to be appear at salinity of 35.5 ‰. Type of demersal fish schooling in Tarakan waters was mostly emerged in form of horizontal contained in the bottom of ocean

Study of Marine Fishing Grounds Based on the Content of Chlorophyll-a and Sea Surface Temperature Via Satellite Imagery of Aqua MODIS of Marine Areas of Rokan Hilir Regency

The study was conducted in October-December 2013. The area of studywas in Rokan Hilir coastal waters, while data analysis was done in the PhysicalOceanography Laboratory, University of Riau, Pekanbaru. The method used wassurvey method with the materials were obtained from the imagenary satellite byusing Aqua MODIS level 1b. The analysis was supported by using software ofEnvi 4.5 and Arcgis 10.The result showed the chlorophyl-a concentration in Rokan Hilir regencywaters varied for each month. In October the value of chlorophyl-a ranged from0.211-1.736 mg/m³, in November the concentration ranged from 0.217-1.731mg/m³, whereas in December the value ranged from 0,286-1,914 mg/m³. The seasurface temperature for each month showed as follows: in Oktober 25°c to 30°c,in November 26°c to 30°c, in December 29°c to 32°c, respectively. The visualanalysis showed that the most potential fishing grounds were found along thecoastal areas of Sinaboi, Sei Nyamuk, and Pulau Halang

Simulation of Generalized Space-Time Autoregressive with Exogenous Variables Model with X Variable of Type Metric

One of the models time series which also involves spatial aspects (spatio-temporal) is Generalized Space Time Autoregressive (GSTAR). Until now, GSTAR modelling don\u27t involve metric-type, which is called GSTARX. Parameter estimation for spatio temporal modeling is still limited by using Ordinary Least Square (OLS) which is less efficient because the residuals are correlated. Generalized Least Square (GLS) is one of the alternative methods for parameter estimation residuals are correlated. In this study would like to looking at the efficiency of GLS estimation method is compared with OLS to correlated data in GSTARX model. Simulation results show that the estimation GLS method is more efficient than using OLS if residual correlated

Sources of Stress among Parents of Children with Intellectual Disabilities: A Preliminary Investigation in Saudi Arabia

This study identified differences in sources of stress between parents of male children with intellectual disabilities in Saudi Arabia. Seventeen pairs of parents completed the Parent Stress Index (Abidin, 1995). Each pair of parents had a male child diagnosed with intellectual disability who either attended an institute for male children with intellectual disability or a special education classroom attached to a public elementary general school in Riyadh, the capital of Saudi Arabia. Results indicated higher perceived stress levels in Saudi mothers compared to fathers regarding the overall parent-child relationship, the child’s individual characteristics, and the parent’s characteristics. Implications provide research-based data for stakeholders in the development of appropriate and effective programs for parents of children with intellectual disabilities, especially for mothers. These data will inform professionals in Saudi Arabia of needed services for parents of children with intellectual disabilities and potentially encourage further study of families of children with intellectual disabilities in Saudi Arabia