CupQ: A New Clinical Literature Search Engine

A new clinical literature search engine, called CupQ, is presented. It aims to help clinicians stay updated with medical knowledge. Although PubMed is currently one of the most widely used digital libraries for biomedical information, it frequently does not return clinically relevant results. CupQ utilizes a ranking algorithm that filters non-medical journals, compares semantic similarity between queries, and incorporates journal impact factor and publication date. It organizes search results into useful categories for medical practitioners: reviews, guidelines, and studies. Qualitative comparisons suggest that CupQ may return more clinically relevant information than PubMed. CupQ is available at https://cupq.io/

On the Consistency of Average Embeddings for Item Recommendation

A prevalent practice in recommender systems consists in averaging item embeddings to represent users or higher-level concepts in the same embedding space. This paper investigates the relevance of such a practice. For this purpose, we propose an expected precision score, designed to measure the consistency of an average embedding relative to the items used for its construction. We subsequently analyze the mathematical expression of this score in a theoretical setting with specific assumptions, as well as its empirical behavior on real-world data from music streaming services. Our results emphasize that real-world averages are less consistent for recommendation, which paves the way for future research to better align real-world embeddings with assumptions from our theoretical setting.Comment: 17th ACM Conference on Recommender Systems (RecSys 2023

Natural Language Processing for the identification of Human factors in aviation accidents causes: An application to the SHEL methodology

Accidents in aviation are rare events. From them, aviation safety management systems take fast and effective remedy actions by performing the analysis of the root causes of accidents, most of which are proved to be human factors. Since the current standard relies on the manual classification performed by trained staff, there are no technical standards already defined for automated human factors identification. This paper considers this issue, proposing machine learning techniques by leveraging on the state-of-the-art technologies of Natural Language Processing. The techniques are then adapted to the Software Hardware Environment Liveware (SHEL) standard accident causality model and tested on a set of real accidents. The computational results show the accuracy and effectiveness of the proposed methodology. Furthermore, the application of the methodology to real documents checked by experts estimates a reduction of the time needed for at least 30% compared to the standard methods of human factors identification

Word vector embeddings hold social ontological relations capable of reflecting meaningful fairness assessments

Programming artificial intelligence (AI) to make fairness assessments of texts through top-down rules, bottom-up training, or hybrid approaches, has presented the challenge of defining cross-cultural fairness. In this paper a simple method is presented which uses vectors to discover if a verb is unfair (e.g., slur, insult) or fair (e.g., thank, appreciate). It uses already existing relational social ontologies inherent in Word Embeddings and thus requires no training. The plausibility of the approach rests on two premises. That individuals consider fair acts those that they would be willing to accept if done to themselves. Secondly, that such a construal is ontologically reflected in Word Embeddings, by virtue of their ability to reflect the dimensions of such a perception. These dimensions being: responsibility vs. irresponsibility, gain vs. loss, reward vs. sanction, joy vs. pain, all as a single vector (FairVec). The paper finds it possible to quantify and qualify a verb as fair or unfair by calculating the cosine similarity of the said verb’s embedding vector against FairVec - which represents the above dimensions. We apply this to Glove and Word2Vec embeddings. Testing on a list of verbs produces an F1 score of 95.7, which is improved to 97.0. Lastly, a demonstration of the method’s applicability to sentence measurement is carried out.This research was funded by the European Union’s Horizon 2020 research and innovation programme under the Next Generation Internet TRUST grant agreement no. 825618

Automated item difficulty modeling with test item representations

This work presents a study that used distributed language representations of test items to model test item difficulty. Distributed language representations are low-dimensional numeric representations of written language inspired and generated by artificial neural network architecture. The research begins with a discussion of the importance of item difficulty modeling in the context of psychometric measurement. A review of the literature synthesizes the most recent automated approaches to item difficulty modeling, introduces distributed language representations, and presents relevant predictive modeling methods. The present study used an item bank from a certification examination in a scientific field as its data set. The study first generated and assessed the quality of distributed item representations with a multi-class similarity comparison. Then, the distributed item representations were used to train and test predictive models. The multi-class similarity task showed that the distributed representations of items were more similar on average to items within their content domain versus outside of their domain in 14 out of 25 domains. The prediction task did not produce any meaningful predictions from the distributed representations. The study ends with a discussion of limitations and potential avenues for future research

Word vector embeddings hold social ontological relations capable of reflecting meaningful fairness assessments

Programming artificial intelligence (AI) to make fairness assessments of texts through top-down rules, bottom-up training, or hybrid approaches, has presented the challenge of defining cross-cultural fairness. In this paper a simple method is presented which uses vectors to discover if a verb is unfair (e.g., slur, insult) or fair (e.g., thank, appreciate). It uses already existing relational social ontologies inherent in Word Embeddings and thus requires no training. The plausibility of the approach rests on two premises. That individuals consider fair acts those that they would be willing to accept if done to themselves. Secondly, that such a construal is ontologically reflected in Word Embeddings, by virtue of their ability to reflect the dimensions of such a perception. These dimensions being: responsibility vs. irresponsibility, gain vs. loss, reward vs. sanction, joy vs. pain, all as a single vector (FairVec). The paper finds it possible to quantify and qualify a verb as fair or unfair by calculating the cosine similarity of the said verb’s embedding vector against FairVec - which represents the above dimensions. We apply this to Glove and Word2Vec embeddings. Testing on a list of verbs produces an F1 score of 95.7, which is improved to 97.0. Lastly, a demonstration of the method’s applicability to sentence measurement is carried out.This research was funded by the European Union’s Horizon 2020 research and innovation programme under the Next Generation Internet TRUST grant agreement no. 825618