mElimu is a technology company with a Learning Management Platform that helps established universities and colleges in emerging markets to offer e-Learning. mElimu provides a cost-effective, scalable, and end-to-end learning management system with patent pending technologies that is uniquely ...

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A Complete Learning Solution for All Your eLearning Needs Know us We are mElimu, an EdTech enterprise focused on revolutionizing the mobile-first learning technology by strengthening anytime, anywhere, and on any device learning across industry verticals. mElimu stands for Mobile Education ...

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This company is providing eelearning, m-learning, online exam platform services to the school, Higher Education, &amp; Training institutes and corporates in India &amp; other countries as well. We provide comprehensive eLearning and AI Proctor online exam solutions for higher education, skill development and corporate training space. mElimu ...

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mElimu is a technology company with a Learning Management Platform that helps established universities and colleges in emerging markets to offer e-Learning

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Leveraging Natural Supervision: Background and Self-Supervised Language Pretraining

Authors: (1) Nowshin Nawar Arony; (2) Ze Shi Li; (3) Bowen Xu; (4) Daniela Damian. Table of Links Abstract & Introduction Motivation Related Work Methodology A Taxonomy of Inclusiveness Inclusiveness Concerns in Different Types of Apps Inclusiveness Across Different Sources of User Feedback Automated Identification of Inclusiveness User Feedback Discussion Conclusion & References 10 CONCLUSION Our study follows a socio-technical grounded theory approach to gain a deeper understanding of inclusiveness related user feedback from end users. Across manual analysis of over 23K user feedback posts from Reddit, Twitter, and Google Play Store regarding 50 popular for profit apps, we build a taxonomy of inclusiveness. Our taxonomy has six main categories, including fairness, technology, privacy, demography, usability, and other human values. The classifier that we train on our data shows that we can automatically identify inclusiveness related feedback among general user feedback. Our results indicate to practitioners that these online sources contain a rich trove of inclusiveness feedback that organizations should consider to build more inclusive software products for diverse end users. We also present our labelled dataset that researchers can use to refine tooling to better support practitioners. REFERENCES [1] A. Savidis and C. Stephanidis, “Inclusive development: Software engineering requirements for universally accessible interactions,” Interacting with Computers, vol. 18, no. 1, pp. 71–116, 2006. [2] J. Shepherd, “23 essential twitter statistics you need to know in 2023,” https://thesocialshepherd.com/blog/twitterstatistics#: :text=TwitterMay 2023. [3] T. Gaurdian, “Twitter apologises for ’racist’ image-cropping algorithm,” https://www.theguardian.com/technology/2020/sep/21/twitterapologises-for-racist-image-cropping-algorithm, 2020. [4] Z. S. Li, M. Sihag, N. N. Arony, J. B. Junior, T. Phan, N. Ernst, and D. Damian, “Narratives: the unforeseen influencer of privacy concerns,” in 2022 IEEE 30th International Requirements Engineering Conference (RE). IEEE, 2022, pp. 127–139. [5] M. Shahin, M. Zahedi, H. Khalajzadeh, and A. R. Nasab, “A study of gender discussions in mobile apps,” arXiv preprint arXiv:2303.09808, 2023. [6] A. Alshayban, I. Ahmed, and S. Malek, “Accessibility issues in android apps: state of affairs, sentiments, and ways forward,” in Proceedings of the ACM/IEEE 42nd International Conference on Software Engineering, 2020, pp. 1323–1334. [7] H. Khalajzadeh, M. Shahin, H. O. Obie, P. Agrawal, and J. Grundy, “Supporting developers in addressing human-centric issues in mobile apps,” IEEE Transactions on Software Engineering, 2022. [8] J. Tizard, T. Rietz, X. Liu, and K. Blincoe, “Voice of the users: an extended study of software feedback engagement,” Requirements Engineering, vol. 27, no. 3, pp. 293–315, 2022. [9] R. 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Lee, and K. Toutanova, “Bert: Pretraining of deep bidirectional transformers for language understanding,” arXiv preprint arXiv:1810.04805, 2018. [33] Y. Liu, M. Ott, N. Goyal, J. Du, M. Joshi, D. Chen, O. Levy, M. Lewis, L. Zettlemoyer, and V. Stoyanov, “Roberta: A robustly optimized bert pretraining approach,” arXiv preprint arXiv:1907.11692, 2019. [34] A. Conneau, K. Khandelwal, N. Goyal, V. Chaudhary, G. Wenzek, F. Guzman, E. Grave, M. Ott, L. Zettlemoyer, and V. Stoyanov, ´ “Unsupervised cross-lingual representation learning at scale,” arXiv preprint arXiv:1911.02116, 2019. [35] M. Lewis, Y. Liu, N. Goyal, M. Ghazvininejad, A. Mohamed, O. Levy, V. Stoyanov, and L. Zettlemoyer, “Bart: Denoising sequence-to-sequence pre-training for natural language generation, translation, and comprehension,” arXiv preprint arXiv:1910.13461, 2019. [36] M. Sihag, Z. S. Li, A. Dash, N. N. Arony, K. Devathasan, N. Ernst, A. B. Albu, and D. 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Applied qualitative research design: A total quality framework approach. New York, NY, US: The Guilford Press, 2015, pages: xviii, 398. This paper is under CC 4.0 license. available on arxiv

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