Responsible Data Science: Select Proceedings of ICDSE 2021

222 Pages·2022·7.156 MB·English

by  Jimson Mathew, G. Santhosh Kumar, Deepak P., Joemon M. Jose

Lecture Notes in Electrical Engineering 940
Jimson Mathew
G. Santhosh Kumar
Deepak P.
Joemon M. Jose   Editors
Responsible
Data Science
Select Proceedings of ICDSE 2021
Lecture Notes in Electrical Engineering
Volume 940
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Alaa Khamis, German University in Egypt El Tagamoa El Khames, New Cairo City, Egypt
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Yong Li, Hunan University, Changsha, Hunan, China
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Tan Cher Ming, College of Engineering, Nanyang Technological University, Singapore, Singapore
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· · ·
Jimson Mathew G. Santhosh Kumar Deepak P.
Joemon M. Jose
Editors
Responsible Data Science
Select Proceedings of ICDSE 2021
Editors
Jimson Mathew G. Santhosh Kumar
Department of Computer Science Department of Computer Science
and Engineering Cochin University of Science
Indian Institute of Technology Patna and Technology
Patna, Bihar, India Cochin, Kerala, India
Deepak P. Joemon M. Jose
School of Electronics, Electrical School of Computing Science
Engineering and Computer Science University of Glasgow
Queen’s University Belfast Glasgow, UK
Belfast, UK
ISSN 1876-1100 ISSN 1876-1119 (electronic)
Lecture Notes in Electrical Engineering
ISBN 978-981-19-4452-9 ISBN 978-981-19-4453-6 (eBook)
https://doi.org/10.1007/978-981-19-4453-6
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Contents
End-to-End Hierarchical Approach for Emotion Detection
in Short Texts ..................................................... 1
Georgios Hadjiharalambous, Kacper Beisert, and Joemon M. Jose
Towards an Enhanced Understanding of Bias in Pre-trained
Neural Language Models: A Survey with Special Emphasis
on Affective Bias .................................................. 13
Anoop K., Manjary P. Gangan, Deepak P., and Lajish V. L.
Exploring Rawlsian Fairness for K-Means Clustering ................. 47
Stanley Simoes, Deepak P., and Muiris MacCarthaigh
Hybrid Explainable Educational Recommender Using
Self-attention and Knowledge-Based Systems for E-Learning
in MOOC Platforms ............................................... 61
Mehbooba P. Shareef, Linda Rose Jimson, and Babita R. Jose
An Improved Recommendation System with Aspect-Based
Sentiment Analysis ................................................ 75
Seema Safar, Babita R. Jose, and T. Santhanakrishnan
Exploring Biomarker Identification and Mortality Prediction
of COVID-19 Patients Using ML Algorithms ......................... 89
Rajan Singh and Prashant K. Srivastava
COVID-19 Cases Prediction Based on LSTM and SIR Model
Using Social Media ................................................ 111
Aakansha Gupta and Rahul Katarya
Joint Geometrical and Statistical Alignment Using Triplet Loss
for Deep Domain Adaptation ....................................... 119
R. Satya Rajendra Singh, Rakesh Kumar Sanodiya, and P. V. Arun
v
vi Contents
Virtual Try-On Using Style Transfer ................................ 131
Ravi Ranjan Prasad Karn, Rakesh Kumar Sanodiya,
Eswara Surya Chandaluri, S. Suryavardan, L Ranajith Reddy,
and Leehter Yao
Attention Mechanism in Convolutional Recurrent Neural Network
for Improving Recognition Accuracy in Printed Devanagari Text ...... 141
Shaheera Saba Mohd Naseem Akhter and Priti P. Rege
Joint Learning for Multitasking Models ............................. 155
Ajai John Chemmanam and Bijoy A. Jose
A CNN Approach for Detecting Red and White Lesions in Retinal
Fundus Images .................................................... 169
Rajesh Kumar and K. V. Pramod
Predicting IMDB Movie Ratings Using RoBERTa Embeddings
and Neural Networks .............................................. 181
Anagha Jose and Sandhya Harikumar
Domain-Specific Type-Safe APIs for Hierarchical Scientific Data
with Modern C++ ................................................. 191
William F. Godoy, Addi Malviya Thakur, and Steven E. Hahn
Kernelized Transfer Joint Matching for Unsupervised Domain
Adaptation ........................................................ 205
A. K. Devika, Rakesh Kumar Sanodiya, and Babita R. Jose
About the Editors
Jimson Mathew is currently a professor in the Department of the Computer Science
and Engineering, Indian Institute of Technology Patna, India. He received a master’s
in computer engineering from Nanyang Technological University, Singapore, and a
Ph.D. degree in computer engineering from the University of Bristol, Bristol, UK.
He has held positions with the Centre for Wireless Communications, the National
University of Singapore, Bell Laboratories Research Lucent Technologies North
Ryde, Australia, Royal Institute of Technology KTH, Stockholm, Sweden, and
Department of Computer Science, University of Bristol, UK. He is a Senior Member
of IEEE. He has previously served as Guest Editor for ACM TECS. He also regu-
larly serves on the program committee of top international conferences and holds
multiple patents. His research interests include fault-tolerant computing, computer
vision, machine learning, and IoT systems.
G. Santhosh Kumar is a full Professor at the Department of Computer Science,
Cochin University of Science and Technology, Kerala, India. His research interests
include cyber-physical systems, machine learning, and natural language processing.
He is a senior member of the IEEE and the ACM, published several publications,
and co-authored a book on Data Science.
Deepak P. is an Associate Professor of Computer Science at Queen’s University
Belfast (UK) and an adjunct faculty member at IIT Madras (India). His research
interests include ethics for machine learning, natural language processing, and infor-
mation retrieval. He is a senior member of the IEEE and the ACM and has authored
over 100 publications, authored/edited three books, and is an inventor on over 10
patents.
Joemon M. Jose has been an active researcher in information retrieval (IR) since
1993 and has published over 300 journal and conference articles on information
retrieval. He, along with co-authors, has received best paper/student paper awards
at leading conferences, including ACM SIGIR, IIiX, CHIIR, MMM, and the BCS
ECIR. He has supervised, as primary supervisor, 20 Ph.D. students and over 20 RAs
vii
viii AbouttheEditors
and postdoctoral researchers. He has chaired several conferences, was one of the
program committee chairs for the ECIR 2017 and 2020 conferences, regularly acts
as a primary reviewer for A/A* conferences, and has attracted over 3M pounds in
research funding.
End-to-End Hierarchical Approach
for Emotion Detection in Short Texts
Georgios Hadjiharalambous, Kacper Beisert, and Joemon M. Jose
1 Introduction
Emotion significantly affects our decision-making process and plays an important
role in our daily lives. As large amounts of textual documents are being created and
circulated, there is a need to understand the sentiments and emotional orientation
of the text. Real-life applications, such as reputation management, human–computer
interaction, or understanding social responses to events [1], would benefit from emo-
tion classification. Hence, the task of automatic identification of distinct emotions
expressed in a text has been gaining increased attention by researchers [2–7]. The
focus of emotion classification is to classify each sentence into one or several cate-
gories within a predefined emotion set.
In the last decade, a considerable amount of research has been directed at detect-
ing sentiments in text, and several effective approaches have been developed [1].
However, emotion classification continues to pose a significantly greater challenge,
especially in short texts. Social media streams, e.g., Twitter, generate vast amounts
of real-time data, making them an important study area. Tweets are a maximum of
280 characters in length and are considered to capture meaningful and informative
messages of the sender, which often contain indicators of emotions expressed by
their senders. Detecting emotions in such short texts is a challenging issue, and few
studies have aimed at it so far [1, 8, 9]. However, current approaches fail to effectively
detect emotions from short texts due to the linguistic incompleteness of short social
media texts. We argue that it is essential to develop an end-to-end model to identify
B
G. Hadjiharalambous · K. Beisert · J. M. Jose ( )
School of Computing Science, University of Glasgow, Glasgow G12 8QQ, UK
e-mail: [e-mail]
G. Hadjiharalambous
e-mail: [e-mail]
K. Beisert
e-mail: [e-mail]
© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 1
J. Mathew et al. (eds.), Responsible Data Science, Lecture Notes
in Electrical Engineering 940, https://doi.org/10.1007/978-981-19-4453-6_1