The Teaching Learning Centre at SRM University-AP conducted a 4-day face-to-face Faculty Orientation programme for the newly joined faculty members from January 17 – 20, 2023. Honourable Vice Chancellor, Prof. Manoj K Arora delivered an introductory talk expounding on the larger goals of the University. The programme aimed at seamlessly aligning faculty activities with the University’s ideology & philosophy.

The workshop included a series of hands-on sessions on Active Learning, Project Based Learning, Technology-enabled learning, Outcome-based learning, Curriculum design, development and delivery, new ways of assessments, Classroom communication and Student engagement facilitated by Dr Balaguruprasad N and Dr Anupama G, faculty members of the Teaching Learning Centre.

More than 50 new faculty members attended and benefited from the programme by working together in experiential activities, creating new lessons using innovative pedagogies and designing innovative assessments among other teaching-learning practices. The participating faculty members also benefited from the University student counsellor, Ms Liza Hazarika and Dr Veena Kumar from the University of Maryland, Global Campus.

An Image caption generator system implies the detection of the image as well as producing the caption with natural language processing by the computer. This is a tedious job. Image caption generator systems can solve various problems, such as self-driving cars, aiding the blind, etc.

The recent research at the Department of Computer Science and Engineering proposes a model to generate the captions for an image using ResNet and Long Short-Term Memory. Assistant Professors Dr Morampudi Mahesh Kumar and Dr V Dinesh Reddy have published the paper Image Description Generator using Residual Neural Network and Long-Short-Term Memory in the Computer Science Journal of Moldova with an impact factor of 0.43.

The captions or descriptions for an image are generated from an inverse dictionary formed during the model’s training. Automatic image description generation is helpful in various fields like picture cataloguing, blind persons, social media, and various natural language processing applications.

Despite the numerous enhancements in image description generators, there is always a scope for development. Taking advantage of the larger unsupervised data or weakly supervised methods is a challenge to explore in this area, and this is already there among the future plan of the researchers. Another major challenge could be generating summaries or descriptions for short videos. This research work can also be extended to other sets of natural languages apart from English.

Abstract

Human beings can describe scenarios and objects in a picture through vision easily, whereas performing the same task with a computer is a complicated one. Generating captions for the objects of an image helps everyone to understand the scenario of the image in a better way. Instinctively describing the content of an image requires the apprehension of computer vision as well as natural language processing. This task has gained huge popularity in the field of technology, and there is a lot of research work being carried out. Recent works have been successful in identifying objects in the image but are facing many challenges in generating captions to the given image accurately by understanding the scenario. To address this challenge, we propose a model to generate the caption for an image. Residual Neural Network (ResNet) is used to extract the features from an image. These features are converted into a vector of size 2048. The caption generation for the image is obtained with Long Short-Term Memory (LSTM). The proposed model was experimented with on the Flickr8K dataset and obtained an accuracy of 88.4%. The experimental results indicate that our model produces appropriate captions compared to the state of art models.

It is a prerequisite for a country like India showcasing vast cultural, social, political and economic diversity, for adopting an unprejudiced reservation system to ensure equal and just representation of varied communities in the political decision-making process of the country. But many communities that have been historically disadvantageous(SC/ST) still struggle to receive their right to representation. Dr Ugen Bhutia, Assistant Professor, Department of Liberal Arts, has published a paper titled “The Limbu–Tamang Communities of Sikkim History and Future of Their Demand for Reservation” in the Journal Economic and Political Weekly. The paper provides a comprehensive outlook on the complex history of the communities in Sikkim and past events that have cumulated in their demand for representation in the democratic polity of the country. The future direction of the demand for reservation and its prospective outcomes have also been emulated.

Abstract

Since its merger in 1975 with the Indian union, one of the major sociopolitical issues in Sikkim has been the demand for reservation in the state legislative assembly for two communities—Limbu and Tamang. The demand of reservation for the Limbus and Tamangs crystallised in Sikkim when these communities were notified as Scheduled Tribes under the Scheduled Castes and Scheduled Tribes Orders (Amendment) Act, 2002. The history and future of this political demand has been analysed.