Potentially idiomatic expressions (PIEs) carry meanings inherently tied to the everyday experience of a given language community. As such, they constitute an interesting challenge for assessing the linguistic (and to some extent cultural) capabilities of NLP systems. In this paper, we present XMPIE, a parallel multilingual and multimodal dataset of potentially idiomatic expressions. The dataset, containing 34 languages and over ten thousand items, allows comparative analyses of idiomatic patterns among language-specific realisations and preferences in order to gather insights about shared cultural aspects. This parallel dataset allows evaluation of language model performance for a given PIE in different languages and whether idiomatic understanding in one language can be transferred to another. Moreover, the dataset supports the study of PIEs across textual and visual modalities, to measure to what extent PIE understanding in one modality transfers or implies in understanding in another modality (text vs. image). The data was created by language experts, with both textual and visual components crafted under multilingual guidelines, and each PIE is accompanied by five images representing a spectrum from idiomatic to literal meanings, including semantically related and random distractors. The result is a high-quality benchmark for evaluating multilingual and multimodal idiomatic language understanding.

There is growing interest in developing reliable, non-invasive, and cost-effective methods for early diagnosis of neurodegenerative diseases such as Mild Cognitive Impairment (MCI) and Alzheimer’s Disease (AD). In this regard, handwriting-based tasks have shown potential in differentiating MCI and AD patients from healthy controls (HCs). However, previous work has reported mixed results when using different symbols and data representations. We address this research gap by developing computational models (convolutional and recurrent neural networks) to differentiate MCI and AD from HCs with off-line (scanned images) and on-line (discrete time series) house drawings. Notably, we observed that augmenting on-line data and then converting it to off-line format, a method we refer to as "OnOff-line", yielded the best performance results in binary classification tasks. These findings highlight the effectiveness of on-line representations in capturing handwriting dynamics more accurately. Ultimately, our work opens new avenues for future research to enhance automated diagnostic of MCI and AD from handwriting analysis.

Alzheimer's disease (AD) is a cognitive disorder, marked by memory loss and impaired reasoning, that requires early detection methods to better manage and potentially slow down the disease's progression. Recent advances in machine learning have offered new possibilities for AD detection using handwriting analysis, however previous work has considered only one type of input source, e.g. clock or pentagon drawings. Here we propose to develop an efficient method for detecting AD's early symptoms using Deep Feature Concatenation (DFC) models considering multiple handwriting sources: pentagon drawings, self-reported sentences, and signatures. Substantial performance improvements were observed when considering all input sources together with data augmentation techniques. For example, classification accuracy increased from 60% (best model, without data augmentation) to 80% (DFC and data augmentation). Our findings show that the use of diverse input sources can lead to an efficient and cost-effective method for early AD detection. Looking forward into the future, our study highlights the potential of DFC in supporting home-based healthcare diagnoses which is a crucial step in integrating artificial intelligence into healthcare practices.

Dementia is a disease characterized by memory impairment and a gradual disability in performing daily activities. Automated screening for early detection of dementia can lead to more adequate and timely treatment. Our work focuses on predicting various stages of dementia severity using pre-trained Deep Learning (DL) models and a public Clock Drawing Test (CDT) dataset. However, the relationship between sample size and model performance is not yet well understood. This may lead to an overreliance on a large number of samples for model training, which may eventually deter reliable outcomes. We found that the classification performance of DL models tends to plateau once a certain number of samples is reached, therefore, it is possible to work on a small data regime with DL models in this task. This research not only advances the field of medical image analysis for dementia screening but also offers broader implications for DL applications in healthcare. Ultimately, the understanding of how sample size affects model performance can guide future research and support more intelligent and efficient utilization of DL models in addressing complex health-related challenges.

The rise of Artificial Intelligence has led to advancements in daily life, including applications in industries, telemedicine, farming, and smart cities. It is necessary to have human-AI synergies to guarantee user engagement and provide interactive expert knowledge, despite AI’s success in "less technical" fields. In this article, the possible synergies between humans and AI to explain the development of art history and artistic style transfer are discussed. This study is part of the "Smart Photobooth" project that is able to automatically transform a user’s picture into a well-known artistic style as an interactive approach to introduce the fundamentals of the history of art to the common people and provide them with a concise explanation of the various art painting styles. This study investigates human-AI synergies by combining the explanation produced by an explainable AI mechanism with a human expert’s insights to provide reasons for school students and a larger audience.

peer reviewed