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570 results on '"P., Ramteke"'

3. Detecting Deceptive Dark Patterns in E-commerce Platforms

Author

Ramteke, Arya, Tembhurne, Sankalp, Sonawane, Gunesh, and Bhimanpallewar, Ratnmala N.

Subjects
Computer Science - Information Retrieval, Computer Science - Artificial Intelligence, Computer Science - Computation and Language, Computer Science - Human-Computer Interaction
Abstract

Dark patterns are deceptive user interfaces employed by e-commerce websites to manipulate user's behavior in a way that benefits the website, often unethically. This study investigates the detection of such dark patterns. Existing solutions include UIGuard, which uses computer vision and natural language processing, and approaches that categorize dark patterns based on detectability or utilize machine learning models trained on datasets. We propose combining web scraping techniques with fine-tuned BERT language models and generative capabilities to identify dark patterns, including outliers. The approach scrapes textual content, feeds it into the BERT model for detection, and leverages BERT's bidirectional analysis and generation abilities. The study builds upon research on automatically detecting and explaining dark patterns, aiming to raise awareness and protect consumers.

Published
2024

26. Contrastive Credibility Propagation for Reliable Semi-Supervised Learning

Author

Kutt, Brody, Ramteke, Pralay, Mignot, Xavier, Toman, Pamela, Ramanan, Nandini, Chhetri, Sujit Rokka, Huang, Shan, Du, Min, and Hewlett, William

Subjects
Computer Science - Machine Learning
Abstract

Producing labels for unlabeled data is error-prone, making semi-supervised learning (SSL) troublesome. Often, little is known about when and why an algorithm fails to outperform a supervised baseline. Using benchmark datasets, we craft five common real-world SSL data scenarios: few-label, open-set, noisy-label, and class distribution imbalance/misalignment in the labeled and unlabeled sets. We propose a novel algorithm called Contrastive Credibility Propagation (CCP) for deep SSL via iterative transductive pseudo-label refinement. CCP unifies semi-supervised learning and noisy label learning for the goal of reliably outperforming a supervised baseline in any data scenario. Compared to prior methods which focus on a subset of scenarios, CCP uniquely outperforms the supervised baseline in all scenarios, supporting practitioners when the qualities of labeled or unlabeled data are unknown., Comment: Accepted to AAAI '24

Published
2022

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