Regulatory bodies worldwide are intensifying their efforts to ensure transparency in influencer marketing on social media through instruments like the Unfair Commercial Practices Directive (UCPD) in the European Union, or Section 5 of the Federal Trade Commission Act. Yet enforcing these obligations has proven to be highly problematic due to the sheer scale of the influencer market. The task of automatically detecting sponsored content aims to enable the monitoring and enforcement of such regulations at scale. Current research in this field primarily frames this problem as a machine learning task, focusing on developing models that achieve high classification performance in detecting ads. These machine learning tasks rely on human data annotation to provide ground truth information. However, agreement between annotators is often low, leading to inconsistent labels that hinder the reliability of models. To improve annotation accuracy and, thus, the detection of sponsored content, we propose using chatGPT to augment the annotation process with phrases identified as relevant features and brief explanations. Our experiments show that this approach consistently improves inter-annotator agreement and annotation accuracy. Additionally, our survey of user experience in the annotation task indicates that the explanations improve the annotators' confidence and streamline the process. Our proposed methods can ultimately lead to more transparency and alignment with regulatory requirements in sponsored content detection.

Gain a competitive edge in the world of Computer Vision through this course by learning how to do Smart Data Extraction from Pdf and Images. Gain a competitive edge in the world of Computer Vision through this course by learning how to do Smart Data Extraction from Pdf and Images. The technology landscape of world has brought in cognitive skills at the forefront where major emphasis is on intelligent data extraction. This becomes more complex due to the huge variety of input documents such as pdf document with structured data, scanned pdf document and word document. This course aims to solve this challenging problem by helping you to understand these various formats and then empower you to do smart data extraction using Python, Pandas, OCR, Tesseract, PyTesseract, OpenCV, Spacy and NER concepts.

In recent years, machine learning has made leaps and bounds enabling applications with high recognition accuracy for speech and images. However, other types of data to which these models can be applied have not yet been explored as thoroughly. In particular, it can be relatively challenging to accurately classify single or multi-model, real-time sensor data. Labelling is an indispensable stage of data pre-processing that can be even more challenging in real-time sensor data collection. Currently, real-time sensor data labelling is an unwieldly process with limited tools available and poor performance characteristics that can lead to the performance of the machine learning models being compromised. In this paper, we introduce new techniques for labelling at the point of collection coupled with a systematic performance comparison of two popular types of Deep Neural Networks running on five custom built edge devices. These state-of-the-art edge devices are designed to enable real-time labelling with various buttons, slide potentiometer and force sensors. This research provides results and insights that can help researchers utilising edge devices for real-time data collection select appropriate labelling techniques. We also identify common bottlenecks in each architecture and provide field tested guidelines to assist developers building adaptive, high performance edge solutions.

We contribute to the investigation of possible outcomes of argumentation under semantics formulated using argumentation frameworks (AFs). In particular, we study this question for the labelling-based formulation of such semantics, generalizing previous work which has focused on extensions. In this paper, we restrict attention to the preferred and semi-stable semantics, showing that as long as we have a sufficient number of fresh arguments available, we can in fact argue for anything. That is, for any set of finite labellings there is an AF that enforces exactly this set as the outcome of argumentation.

Given an argumentation framework and a group of agents, the individuals may have divergent opinions on the status of the arguments. If the group needsto reach a common position on the argumentation framework, the question is how the individual evaluations can be mapped into a collective one. Thisproblem has been recently investigated by Caminada and Pigozzi. In this paper, we investigate the behaviour of two of such operators from a socialchoice-theoretic point of view. In particular, we study under which conditions these operators are Pareto optimal and whether they are manipulable.