LAGAN: Deep Semi-Supervised Linguistic-Anthropology Classification with
  Conditional Generative Adversarial Neural Network
Education is a right of all, however, every individual is different than
others. Teachers in post-communism era discover inherent individualism to
equally train all towards job market of fourth industrial revolution. We can
consider scenario of ethnic minority education in academic practices. Ethnic
minority group has grown in their own culture and would prefer to be taught in
their native way. We have formulated such linguistic anthropology(how people
learn)based engagement as semi-supervised problem. Then, we have developed an
conditional deep generative adversarial network algorithm namely LA-GAN to
classify linguistic ethnographic features in student engagement. Theoretical
justification proves the objective, regularization and loss function of our
semi-supervised adversarial model. Survey questions are prepared to reach some
form of assumptions about z-generation and ethnic minority group, whose
learning style, learning approach and preference are our main area of interest.
Rossi Kamal, Zuzana Kubincova
# LAGAN: Deep Semi-Supervised

###### Abstract

Education is a right of all, however, every individual is different than others. Teachers in post-communism era discover inherent individualism to equally train all towards job market of fourth industrial revolution. We can consider scenario ethnic minority education in academic practices. Ethnic minority group such has grown in their own culture and would prefer to be taught in their native way. We have formulated such linguistic anthropology(how people learn)based engagement as semi-supervised problem. Then, we have developed an conditional deep generative adversarial network algorithm namely LA-GAN to classify linguistic ethnographic features in student engagement. Theoretical justification proves the objective, regularization and loss function of our semi-supervised adversarial model. Survey questions are prepared to reach some form of assumptions about z-generation and ethnic minority group, whose learning style, learning approach and preference are our main area of interest.

 Linguistic Anthropology,Semi-Supervised Learning, Ethnicity, Adversarial Training

## I Introduction

Deep neural network is promising technology as it uses deep or layered abstract representation for conceptualizing meaning of raw data with automated manner[1]-[8]. Its coined by often by supervised learning with possible assumption about the presence of huge amount and quality labeled data. However, in practical getting access to labeled data in massive scale is expensive, time-consuming and often requires very expensive human expertise involvement. Therefore, its always a difficult to prepare a deep neural based system with the availability of very limited number of data. However, in practical the presence of unlabeled data is large scale is very practical. Thus, relatively new concept of semi-supervised learning emerges with the promise of exploiting huge unlabeled data to achieve better learning accuracy, given that we have access to limited labeled knowledge. Deep semi-supervised learning resembles thus exploiting classic neural network architectures with classic semi-supervised learning techniques to infer data pattern both labeled and unlabeled sample.Generative Adversarial Network[4]-[8] is recognized for creating synthetic data in the absence of huge labeled data. GAN is resembled by generator and discriminator, in which generators role is to create real-like data, on the other hand discriminator detects the real from fake data. However, in conventional GAN, there is seldom control on data mode being created. Conditional GAN, on the other hand, by putting some contextual condition, such as class class label, data modality eases the data generation.

Linguistic anthropology is a division of social science, which deals with language as social action[9]-[11]. The interdisciplinary domain intersects topics from linguistics, sociology, philosophy, and anthropology.However, knowledge includes verbal and written language as both origin and media of social culture. Thus it is an outward expression of language usage, rather than syntax or grammatical base. In other words, despite underneath phonology or syntax, the way social interactions are expressed in verbal and written media, is denoted as linguistic anthropology.Inevitably, linguistic anthropology is considered a dominant matter in educational institutes as language is the center of speech or written form of communication.Because, the way the speaker interacts with the surrounding plays a key role in mediating knowledge exchange in groups. This also defines the way information exchange occurs in institutional groups and thus helps pupils in interacting in presentation, meeting and in-person towards building a successful career.Our primary research[12]-[20] is focused with education reform in post-colonial era, where there is much a concern over presence of education inequality and absence of education fluidity. Homogeneous society which is living for centuries are now watching different children in class as consequence of arrival of foreign citizen or ethnic groups. Especially, as soon as the communism era ends, ethnic communities are accepted in mainstream schooling. Unfortunately, ethnic communities have been isolated from mainstream school for many decades and they are not ready for job-market in this fourth industrial revolution. We consider that linguistic anthropology features in ethnic community should be taken in consideration for providing them quality education. Ethnic education statistics is collected from classroom observation, in-person or group interview or