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Automatically recognizing emotional intent using facial expression has been a thoroughly investigated topic in the realm of computer vision. Facial Expression Recognition (FER), being a supervised learning task, relies heavily on substantially large data exemplifying various socio-cultural demographic attributes. Over the past decade, several real-world in-the-wild FER datasets that have been proposed were collected through crowd-sourcing or web-scraping. However, most of these practically used datasets employ a manual annotation methodology for labeling emotional intent, which inherently propagates individual demographic biases. Moreover, these datasets also lack an equitable representation of various socio-cultural demographic groups, thereby inducing a class imbalance. Bias analysis and its mitigation have been investigated across multiple domains and problem settings, however, in the FER domain, this is a relatively lesser explored area. This work leverages representation learning based on latent spaces to mitigate bias in facial expression recognition systems, thereby enhancing a deep learning model's fairness and overall accuracy.

The rapid advancement in deep learning over the past decade has transformed Facial Expression Recognition (FER) systems, as newer methods have been proposed that outperform the existing traditional handcrafted techniques. However, such a supervised learning approach requires a sufficiently large training dataset covering all the possible scenarios. And since most people exhibit facial expressions based upon their age group, gender, and ethnicity, a diverse facial expression dataset is needed. This becomes even more crucial while developing a FER system for the Indian subcontinent, which comprises of a diverse multi-ethnic population. In this work, we present InFER, a real-world multi-ethnic Indian Facial Expression Recognition dataset consisting of 10,200 images and 4,200 short videos of seven basic facial expressions. The dataset has posed expressions of 600 human subjects, and spontaneous/acted expressions of 6000 images crowd-sourced from the internet. To the best of our knowledge InFER is the first of its kind consisting of images from 600 subjects from very diverse ethnicity of the Indian Subcontinent. We also present the experimental results of baseline & deep FER methods on our dataset to substantiate its usability in real-world practical applications., Comment: In Proceedings of the 15th International Conference on Agents and Artificial Intelligence Volume 3: ICAART; ISBN 978-989-758-623-1; ISSN 2184-433X, SciTePress, pages 550-557. DOI: 10.5220/0011699400003393

In the present work, experimental study has been carried out to expose the thermal, mechanical, and microstructural properties of low carbon steel as well as to inspects the influence of etchant concentration and etching time on its microstructure. Ultra-low carbon steel, in the form of a sheet, was collected from the Mughal Steel Industry, Peshawar, Pakistan. The sample was chemically etched, using Nital as an etchant, by two different methods: first, by changing the etching time while keeping the composition of etchant the same and second, by keeping the time constant while varying the etchant composition in a range of 5-14 %. The microstructure analysis revealed that ultra-fine grain can be obtained for the etchant composition of 8 % nitric acid in ethanol. Additionally, we noticed that the best etching time, for getting a clear morphology, was 90 s. The X-ray diffraction revealed mainly alpha-iron. Thermal analysis showed a minor weight loss followed by weight gain of 1.31 wt %. Contraction and expansion, observed on the TDA curve, suggested the transformation of BCC to FCC structure. Our results indicated that the specimen is highly ductile, malleable and soft., Comment: 8 pages, 5 figures, 2 tables, Journal of the Chemical Society of Pakistan

Data Warehouse (DW) is an essential part of Business Intelligence. DW emerged as a fast growing reporting and analysis technique in early 1980s. Today, it has almost replaced relational databases. However, with passage of time, static and historic data of DWs could not produce Real Time reporting and analysis, thus giving a way to emerge the Idea of Real Time Data Warehouse (RTDW). Although, there are problems with RTDWs, but with advancement in technology and researchers focus, RTDWs will be able to generate real time reports, analysis and forecasting.