Data discovery and table unionability in particular became key tasks in modern Data Science. However, the human perspective for these tasks is still under-explored. Thus, this research investigates the human behavior in determining table unionability within data discovery. We have designed an experimental survey and conducted a comprehensive analysis, in which we assess human decision-making for table unionability. We use the observations from the analysis to develop a machine learning framework to boost the (raw) performance of humans. Furthermore, we perform a preliminary study on how LLM performance is compared to humans indicating that it is typically better to consider a combination of both. We believe that this work lays the foundations for developing future Human-in-the-Loop systems for efficient data discovery.

Gaze is a crucial social cue in any interacting scenario and drives many mechanisms of social cognition (joint and shared attention, predicting human intention, coordination tasks). Gaze direction is an indication of social and emotional functions affecting the way the emotions are perceived. Evidence shows that embodied humanoid robots endowing social abilities can be seen as sophisticated stimuli to unravel many mechanisms of human social cognition while increasing engagement and ecological validity. In this context, building a robotic perception system to automatically estimate the human gaze only relying on robot's sensors is still demanding. Main goal of the paper is to propose a learning robotic architecture estimating the human gaze direction in table-top scenarios without any external hardware. Table-top tasks are largely used in many studies in experimental psychology because they are suitable to implement numerous scenarios allowing agents to collaborate while maintaining a face-to-face interaction. Such an architecture can provide a valuable support in studies where external hardware might represent an obstacle to spontaneous human behaviour, especially in environments less controlled than the laboratory (e.g., in clinical settings). A novel dataset was also collected with the humanoid robot iCub, including images annotated from 24 participants in different gaze conditions.

Large Language Models (LLMs) have acquired ubiquitous attention for their performances across diverse domains. Our study here searches through LLMs' cognitive abilities and confidence dynamics. We dive deep into understanding the alignment between their self-assessed confidence and actual performance. We exploit these models with diverse sets of questionnaires and real-world scenarios and extract how LLMs exhibit confidence in their responses. Our findings reveal intriguing instances where models demonstrate high confidence even when they answer incorrectly. This is reminiscent of the Dunning-Kruger effect observed in human psychology. In contrast, there are cases where models exhibit low confidence with correct answers revealing potential underestimation biases. Our results underscore the need for a deeper understanding of their cognitive processes. By examining the nuances of LLMs' self-assessment mechanism, this investigation provides noteworthy revelations that serve to advance the functionalities and broaden the potential applications of these formidable language models.

Semantic similarity analysis and modeling is a fundamentally acclaimed task in many pioneering applications of natural language processing today. Owing to the sensation of sequential pattern recognition, many neural networks like RNNs and LSTMs have achieved satisfactory results in semantic similarity modeling. However, these solutions are considered inefficient due to their inability to process information in a non-sequential manner, thus leading to the improper extraction of context. Transformers function as the state-of-the-art architecture due to their advantages like non-sequential data processing and self-attention. In this paper, we perform semantic similarity analysis and modeling on the U.S Patent Phrase to Phrase Matching Dataset using both traditional and transformer-based techniques. We experiment upon four different variants of the Decoding Enhanced BERT - DeBERTa and enhance its performance by performing K-Fold Cross-Validation. The experimental results demonstrate our methodology's enhanced performance compared to traditional techniques, with an average Pearson correlation score of 0.79.