The paper focuses on calculating suitable place names and descriptive tags for large photo collections of visually interesting sights. The core dataset analyzed contains 45 million crowd-sourced geotagged pictures of the Panoramio database. We present several methods for analysis along with machine learning experiments for tag recommendation and suggest a manually built taxonomy of tag categories, based on the analysis of most widely used taglike words in the photo titles, along with their popularities. The methods, selected tags and the taxonomy can be used for building different tourism applications for visually interesting sights.
Ontology visualization is an important component in the support of human-ontology interaction, as it amplifies cognition and offloads cognitive efforts to the human perceptual system. While a significant amount of research efforts has focused on designing and developing various visual layouts and improve performance of large-scale visualizations, the differences in user preferences and cognitive abilities have been largely overlooked. This provides an opportunity to investigate ways to potentially provide more personalized visual support in human-ontology interaction. To this end, this paper demonstrates successful predictions on an individual user's likelihood to succeed in a given task, based on this person's gaze data collected during interaction. Specifically, we show several statistically significant predictions against a baseline classifier when inferring users' success before a given task is actually completed. Moreover, we present results showing that accurate predictions of user success can be achieved early on during user interaction, such as after just a few minutes in some cases. These findings suggest there are ample opportunities throughout various stages of human-ontology interaction where the underlying visual system may adapt in real time to the user's visual needs to provide the most appropriate visualization with the overall goal of possibly increasing user success in a given task.
The management of aviation data is a great challenge in the aviation industry, as they are complex and can be derived from heterogeneous data sources. To handle this challenge, ontologies can be applied to facilitate the modelling of the data across multiple data sources. This paper presents an aviation domain ontology, the ICARUS ontology, which aims at facilitating the semantic description and integration of information resources that represent the various assets of the ICARUS platform and their use. To present the functionality and usability of the proposed ontology, we present the results of querying the ontology using SPARQL queries through three use case scenarios. As shown from the evaluation, the ICARUS ontology enables the integration and reasoning over multiple sources of heterogeneous aviation-related data, the semantic description of metadata produced by ICARUS, and their storage in a knowledge-base which is dynamically updated and provides access to its contents via SPARQL queries.
Music recommender systems (RS) aim to aid people with finding relevant enjoyable music without having to sort through the enormous amount of available content. Music RS often rely on collaborative filtering methods, which however limits predicting capabilities in cold-start situations or for users who deviate from main-stream music preferences. Therefore, this paper evaluates various content-based music recommendation methods that may be used in combination with collaborative filtering to overcome such issues. Specifically, the paper focuses on the ability of lyrics-based embedding methods such as tf-idf, word2vec or bert to estimate songs similarity compared to state-of-the-art audio and meta-data based embeddings. Results indicate that both audio and lyrics methods perform similarly, which may favor lyrics-based approaches due to the much simpler processing. We also show that although lyrics-based methods do not outperform meta-data based approaches, they provide much more diverse, yet reasonably relevant recommendations, which is suitable in exploration-oriented music RS.
Whilst the CIA have been using psychometric profiling for decades, Cambridge Analytica showed that peoples psychological characteristics can be accurately predicted from their digital footprints, such as their Facebook or Twitter accounts. To exploit this form of psychological assessment from digital footprints, we propose machine learning methods for assessing political personality from Twitter. We have extracted the tweet content of Prime Minster Boris Johnsons Twitter account and built three predictive personality models based on his Twitter political content. We use a Multi-Layer Perceptron Neural network, a Naive Bayes multinomial model and a Support Machine Vector model to predict the OCEAN model which consists of the Big Five personality factors from a sample of 3355 political tweets. The approach vectorizes political tweets, then it learns word vector representations as embeddings from spaCy that are then used to feed a supervised learner classifier. We demonstrate the effectiveness of the approach by measuring the quality of the predictions for each trait per model from a classification algorithm. Our findings show that all three models compute the personality trait "Openness" with the Support Machine Vector model achieving the highest accuracy. "Extraversion" achieved the second highest accuracy personality score by the Multi-Layer Perceptron neural network and Support Machine Vector model.
