Demo Data from EDBT 2026 / reviewers in the wild / expert
Sarath S
dblp:252/6946
· DBLP profile ↗
3ranked-venue papers
0as first author
3since 2021 · last 2023
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Human-computer interaction and ubiquitous computing · 2 · 2 since 2021Artificial intelligence and machine learning · 1 · 1 since 2021Graphics, computer vision, multimedia, augmented reality and games · 1 · 1 since 2021Applied, interdisciplinary, general and emerging computing · 1 · 1 since 2021
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Databases, data mining, and information retrieval
1 paper |
Knowledge graphs · 100% | |
| Artificial intelligence
1 paper |
Information extraction and text analysis · 100% |
Topics — the 3 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Natural language and speech › Information extraction and text analysis › document analysis › scholarly text analysis
scientific information extraction |
0.5 | 1 | 2021 | Device Fabrication Knowledge Extraction from Materials Science Literature · AAAI 2021 |
Knowledge graphs
knowledge graph construction |
0.5 | 1 | 2021 | Device Fabrication Knowledge Extraction from Materials Science Literature · AAAI 2021 |
Knowledge graphs › knowledge graph construction › knowledge extraction
entity and relation extraction |
0.1 | 1 | 2021 | Device Fabrication Knowledge Extraction from Materials Science Literature · AAAI 2021 |
Methods — techniques the papers use, named apart from their topics
unsupervised sequence identification · 1.0relation extraction · 1.0entity extraction · 1.0
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2023 | Gender Differences in School Students' Perceptions Towards Engineering: A Case Study From Rural South IndiaabstractGender typecasts have prevailed in higher education since time immemorial. Previous research studies have demonstrated the incongruity in gender representation in almost every higher education sector, including engineering education. Gender-specific variation in perceptions towards engineering education and the engineering profession is a significant area of interest. However, relatively little research has been conducted on gender differences in school students' attitudes and perceptions of engineering. In addition, no such previous study has surveyed students of schools on the rural outskirts. The current study intends to address this research gap by investigating the insights of school students who participated in the ‘Rural Students' Technology Enhancement Program (R-STEP).’ R-STEP was a technical training program organised by the IEEE Education Society (EdSoc) Kerala Chapter exclusively for less-privileged school students from the rural outskirts of Kerala, a southern state of India. The research study critically analysed the R-STEP participants' responses to a questionnaire survey. The questionnaire was in the regional language, Malayalam, comprising five questions each on engineering education and profession. A total of 220 school students from various districts of Kerala participated in the survey. While 62.5% of female survey respondents indicated that they want to be engineers in the future, it was a more significant 86% in the case of males. Rather interestingly, 16.1% of female participants reported that they do not want to be engineers, compared to only 3.1% of males. This research-worthy fivefold difference between male and female attitudes is critically deliberated in the paper, along with other findings from the study. The paper concludes with a few recommendations to address rural school students' gender-specific attitudes towards engineering. N. P. Subheesh, Ayisha E. A, Akash Vijay, R. S. Akshay, Sarath S, K. Yadhukrishna |
EDUCON | 5 |
| 2022 | Rural School Students' Attitudes and Perceptions toward the Engineering Education and the Engineering ProfessionabstractAs the growth of science and technology accelerates, the prospect of engineering as a field of study and profession gains importance. Many school students prefer to study engineering in the future and work as engineers. Examining their views on engineering will help correct frivolous attitudes and proactively revamp engineering education. However, only a few studies have been conducted on secondary school students’ attitudes and perceptions toward engineering, especially from the schools located on rural outskirts. Against this backdrop, this study critically examines rural secondary school students’ attitudes and perceptions of engineering education and the engineering profession. The students were part of the Rural Student Technical Enhancement Program (R-STEP), conducted by the IEEE Education Society. The program was designed as a free-of-cost advanced technical training and skilling session for rural higher secondary students in Kerala, a southern state in India. The data collection for the study was performed by administering a questionnaire containing ten questions, among which five were concerning engineering education, and the other five were on the engineering profession. The questionnaire was prepared in the regional language (Malayalam), considering the respondents’ age group and background. A total of 225 responses were collected from students from various districts of Kerala. The questionnaire responses were then critically analyzed to interpret students’ opinions on engineering education and profession. The research findings show that nearly 60% of rural higher secondary school students sincerely aspire to become engineers. It is also observed that about 50% of students acknowledge that engineers should exhibit problem-solving skills. Another noticeable finding is that about 80% of students accept the significance of practical laboratory-oriented learning in engineering education. On the other hand, more than 30% of students are confused about the significance of mathematics in engineering. Another 30% of students even observed that mathematical aptitude is not obligatory for engineering education. More insights from other questions identified students’ attitudes and perceptions toward the engineering profession. The study concludes with a few curative suggestions and recommendations to enhance rural school students’ awareness of engineering education and profession. Ayisha E. A, Akash Vijay, Parvathy I, Sarath S, N. P. Subheesh |
FIE | 4 |
| 2021 | Device Fabrication Knowledge Extraction from Materials Science LiteratureabstractDevices like solar cells, batteries etc. often comprise of a host of material types including organic, inorganic and hybrid materials. The fabrication procedures for these devices involve screening or designing the right set of materials and then subjecting them to a sequence of operations under very specific conditions. The performance characteristics of a device critically depend on the materials used in its fabrication, the specific operations carried out, their operating conditions and the specific sequence in which they are carried out. The space of potential materials, operations and operating conditions is vast, and selecting the right combination thereof to achieve the desired characteristics is a knowledge intensive activity. A large amount of such device fabrication knowledge is available in the form of publications, patents, company reports and so on. In this paper, we present a system that systematically extracts this knowledge from materials science literature. The extracted knowledge is represented as knowledge graphs conforming to an ontology that can be queried to make informed decisions in device fabrication procedures. The system first identifies the set of relevant paragraphs that contain fabrication knowledge. It then employs state of the art entity and relation extraction models to identify instances of operations, methods, materials, etc. and relations between them. The system then applies an unsupervised algorithm to identify sequences of operations representing fabrication procedures. We applied our system on solar cell fabrication knowledge extraction and achieved good performance. We believe our results provide much needed impetus for further work in this area. Neelanshi Wadhwa, Sarath S, Sapan Shah, Sreedhar Reddy, Pritwish Mitra, Beena Rai |
AAAI | 2 |