Faculty and students of IIITH presented the following papers at the 2020 IEEE 7th International Conference on Data Science and Advanced Analytics (DSAA). The conference was held virtually from 6  – 9  October in Sydney, Australia. 

• Hema Ala, MS by Research student working under the supervision of Dipti Misra Sharma and Ananya, Ph.D student working under the supervision of Manish Srivastava presented a paper on MEE: An Automatic Metric for Evaluation Using Embeddings for Machine Translation.  

The researchers proposed MEE, an approach for automatic Machine Translation (MT) evaluation which leverages the similarity between embeddings of words in candidate and reference sentences to assess translation quality. Unigrams are matched based on their surface forms, root forms and meanings which aids to capture lexical, morphological and semantic equivalence. They performed experiments for MT from English to four Indian Languages (Telugu, Marathi, Bengali and Hindi) on a robust dataset comprising simple and complex sentences with good and bad translations and observed that the proposed metric correlates better with human judgements than the existing widely used metrics.

• Chinmay Bapna, MS scholar working under the supervision of Prof. P Krishna Reddy presented a research paper  on Improving Product Placement in Retail with Generalized High Utility Itemsets. The authors of this paper are Chinmay Bapna, P Krishna Reddy and   Anirban Mondal.

Research work as explained by the author – Product placement in retail has a significant impact on the sales revenue of retailers. Hence, research efforts are being made to improve retailer revenue using high-utility pattern mining based product placement approaches. However, none of these existing approaches has explored generalized high-utility itemset mining for determining product placement in retail. The knowledge of generalized high-utility itemsets extracted from user purchase transactional database in conjunction with a product taxonomy can provide new insights about customer purchase behaviour. This work proposes the generalized utility itemset (GUI) index for retrieving generalized high-utility (revenue) itemsets. We also present a framework, which leverages the GUI index towards retail product placement to improve revenue. Our performance study using real datasets shows the effectiveness of our proposed scheme w.r.t. two existing schemes.

DSAA’2020 featured four prestigious keynote speeches, the research track, the application track, a panel, four special sessions and four traditional/hands-on tutorials  spotlighting important and emerging topics. The IEEE International Conference on Data Science and Advanced Analytics (DSAA) featured its strong interdisciplinary synergy between statistics (sponsored by ASA), computing, and information/intelligence sciences (by IEEE and ACM), and cross-domain interactions between academia and business/industry for data science and analytics.