# Optimization- and AI-based approaches to academic quality quantification for transparent academic recruitment: part 1-model development

arXiv: https://arxiv.org/abs/2305.05460 · HTML: https://mlsanity.com/paper/2305.05460/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 17 references that are in the map.
- First posted on arXiv 3 May 2023.

## Builds on (most cited first, 6 of 6)

- [FaceNet: A Unified Embedding for Face Recognition and Clustering](https://mlsanity.com/paper/1503.03832/) (2015)
- [RankIQA: Learning from Rankings for No-reference Image Quality Assessment](https://mlsanity.com/paper/1707.08347/) (2017)
- [Constrained Monotonic Neural Networks](https://mlsanity.com/paper/2205.11775/) (2022)
- [Learning a distance function with a Siamese network to localize anomalies in videos](https://mlsanity.com/paper/2001.09189/) (2020)
- [Action Quality Assessment using Siamese Network-Based Deep Metric Learning](https://mlsanity.com/paper/2002.12096/) (2020)
- [Embedding Structured Contour and Location Prior in Siamesed Fully Convolutional Networks for Road Detection](https://mlsanity.com/paper/1905.01575/) (2019)

## Abstract (arXiv)

For fair academic recruitment at universities and research institutions, determination of the right measure based on globally accepted academic quality features is a highly delicate, challenging, but quite important problem to be addressed. In a series of two papers, we consider the modeling part for academic quality quantification in the first paper, in this paper, and the case studies part in the second paper. For academic quality quantification modeling, we develop two computational frameworks which can be used to construct a decision-support tool: (i) an optimization-based framework and (ii) a Siamese network (a type of artificial neural network)-based framework. The output of both models is a single index called Academic Quality Index (AQI) which is a measure of the overall academic quality. The data of academics from first-class and average-class world universities, based on Times Higher Education World University Rankings and QS World University Rankings, are assumed as the reference data for tuning model parameters.