Significance Of Life Skills Education

Adolescence is a period when the intellectual, physical, social, emotional and all the capabilities are very high, but, unfortunately, most of the adolescents are unable to utilize their potential to maximum due to various reasons.  They face many emerging issues such as global warming, famines, poverty, suicide, population explosion as well as other issues like alcoholism, drug abuse, sexual abuse, smoking, juvenile delinquency, anti-social acts, etc. that have an adverse effect on them and others too, to a large extent. The cut-throat competition, unemployment, lack of job security, etc. are some of the major concerns for the educated and as a result, they are caught in the mad race.  This new challenge requires immediate and effective responses from a socially responsible system of education. ‘Education’ is important, but education to support and live life better is more important. It has been felt that life skills education bridges the gap between basic functioning and capabilities. It strengthens the ability of an individual to meet the needs and demands of the present society and helps in dealing with the above issues in a manner to get desired behavior practical. Imparting life skill training through inculcating life skill education will help youth to overcome such difficulties in life. The present paper focuses on the importance of life skills education and the benefits of imparting life skill education in our curriculum i.e. developing social, emotional & thinking skills in students, as they are the important building blocks for a dynamic citizen, who can cope up with future challenges, and survive well

Enhanced waters 2D muscle model for facial expression generation

In this paper we present an improved Waters facial model used as an avatar for work published in (Kumar and Vanualailai, 2016), which described a Facial Animation System driven by the Facial Action Coding System (FACS) in a low-bandwidth video streaming setting. FACS defines 32 single Action Units (AUs) which are generated by an underlying muscle action that interact in different ways to create facial expressions. Because FACS AU describes atomic facial distortions using facial muscles, a face model that can allow AU mappings to be applied directly on the respective muscles is desirable. Hence for this task we choose the Waters anatomy-based face model due to its simplicity and implementation of pseudo muscles. However Waters face model is limited in its ability to create realistic expressions mainly the lack of a function to represent sheet muscles, unrealistic jaw rotation function and improper implementation of sphincter muscles. Therefore in this work we provide enhancements to the Waters facial model by improving its UI, adding sheet muscles, providing an alternative implementation to the jaw rotation function, presenting a new sphincter muscle model that can be used around the eyes and changes to operation of the sphincter muscle used around the mouth

Feature Map Upscaling to Improve Scale Invariance in Convolutional Neural Networks

Efforts made by computer scientists to model the visual system has resulted in various techniques from which the most notable has been the Convolutional Neural Network (CNN). Whilst the ability to recognise an object in various scales is a trivial task for the human visual system, it remains a challenge for CNNs to achieve the same behaviour. Recent physiological studies reveal the visual system uses global-first response strategy in its recognition function, that is the visual system processes a wider area from a scene for its recognition function. This theory provides the potential for using global features to solve transformation invariance problems in CNNs. In this paper, we use this theory to propose a global-first feature extraction model called Stacked Filter CNN (SFCNN) to improve scale-invariant classification of images. In SFCNN, to extract features from spatially larger areas of the target image, we develop a trainable feature extraction layer called Stacked Filter Convolut ions (SFC). We achieve this by creating a convolution layer with a pyramid of stacked filters of different sizes. When convolved with an input image the outputs are feature maps of different scales which are then upsampled and used as global features. Our results show that by integrating the SFC layer within a CNN structure, the network outperforms traditional CNN on classification of scaled color images. Experiments using benchmark datasets indicate potential effectiveness of our model towards improving scale invariance in CNN networks

QSAR Studies of Flavonoids Derivatives for Antioxidant and Antimicrobial Activity

The biological activity and the molecular structures are the two main aspects for the QSAR study of a specific compound, which leads to generate a new chemical moiety. The Quantitative Structure Activity Relationship (QSAR) paradigm is based on the assumption that there is an underlying relationship between the molecular structure and biological activity. Physico-chemical properties were calculated employing the modeling software Win CAChe 6.1. The calculated descriptors were conformational minimum energies (CME) , HOMO energy, LUMO energy, heat of formation (HF), ionization potential (IP), molar refractivity(MR), Shape Index (basic kappa, order 1) (SI1), log P, electron affinity (EA), solvent accessible surface area (SAS). In random selection, 18 compounds were in training set and 10 in test set. Subsequently with the stepwise multiple linear regression analysis was carried out to achieve the best models. The equation generated was validated. The selected QSAR model showed correlation coefficient R2 0.7609, and cross-validated squared correlation coefficient Q2   of 0.5041 for antioxidant activity  ANOVA of predicted value for  Variance were found as 0.063638, 201.73 and 0.112396 for group -1.51851, 86.27 and -8.151 respectively. Source of Variation for between and within group i.e. SS value 40522.91 df value 2, MS value 20261.45 F value 301.0527 and p-value 1.01E-17. The HOMO-LUMO range were 73.149 to 84.775, Molar refractivity range was observed -7.409 to -8.84, Predicted value range was -2.52559 to -2.98191 for compound V1 to V5 and R1, R2. The result of study indicated that C5 [1-(2- hydroxyphenyl)-5-phenylpenta-2,4-dien-1-one]; is only inactive against Streptococcus mutans. All 3-hydroxyflavone derivatives exhibited their MIC to be in range of 250-125 µg/ml., 2,3-dihydroflavan-3-ol derivatives exhibited their MIC to be in  range of  1000- 500 µg/ml. The chalcone derivatives exhibited their MIC to be at 250µg/ml. Keywords: QSAR, Streptococcus mutans, Win CAChe 6.1 and antioxidant activit