Generalized Zero-Shot Learning via Synthesized Examples

We present a generative framework for generalized zero-shot learning where the training and test classes are not necessarily disjoint. Built upon a variational autoencoder based architecture, consisting of a probabilistic encoder and a probabilistic conditional decoder, our model can generate novel exemplars from seen/unseen classes, given their respective class attributes. These exemplars can subsequently be used to train any off-the-shelf classification model. One of the key aspects of our encoder-decoder architecture is a feedback-driven mechanism in which a discriminator (a multivariate regressor) learns to map the generated exemplars to the corresponding class attribute vectors, leading to an improved generator. Our model's ability to generate and leverage examples from unseen classes to train the classification model naturally helps to mitigate the bias towards predicting seen classes in generalized zero-shot learning settings. Through a comprehensive set of experiments, we show that our model outperforms several state-of-the-art methods, on several benchmark datasets, for both standard as well as generalized zero-shot learning.Comment: Accepted in CVPR'1

Factors Influencing Cloud Computing Adoption by Small and Medium-Sized Enterprises (SMEs) In India

The purpose of this paper is to examine and assess the determinants of cloud computing adoption by small and medium-sized enterprises (SMEs) in an Indian context through an integrated research framework. A questionnaire based survey method was used to collect data from 271 SMEs from the northern region of India. An integrated model founded on Technology Acceptance Model (TAM), Diffusion of Innovation (DOI) and Technology-Organization-Environment (TOE) was proposed to test ten hypotheses through a confirmatory factor analysis and structural equation modelling. The analysis reveals that relative advantage, security concerns, top management support, external pressure and service providers’ support are the significant factors which influence intention to adopt cloud computing by SMEs in India. The integrated research framework used and validated in this study is intended to enhance the predictive power of the ensuing model. The empirically validated research model and the instrument used for data collection can be adopted in other similar studies. The findings of this study can be useful for consumers, practitioners, and policy makers who are involved in utilizing, spreading and promoting the adoption of cloud computing among organizations, especially the SMEs. Available at: https://aisel.aisnet.org/pajais/vol9/iss3/3

Research Trends in Annals of Library and Information Studies (ALIS): A Bibliometric Review from 2011-2021

The study uses various bibliometric techniques to examine papers published in the Annals of Library and Information Studies (ALIS) from 2011 to 2021. Scopus database was used to gather the necessary information. Furthermore, the research productivity was measured using various parameters such as year-by-year distribution of publications with citations, RCI, AGR, ACPP, CAI, Citation analysis, and author, institution, and country collaboration. The findings of the study, the number of articles published varies with time, with the highest number of articles published in 2014. Two authors published the most research papers (152), followed by single authors (108). B. K. Sen was the most prolific author, with 19 publications and 34 citations. India\u27s Council of Scientific and Industrial Research contributed 30 papers, which were ranked first among the top ten most effective institutions. As per the distribution of output by country, India contributed the most, with 242 research papers, followed by Nigeria with 24 research papers