177,063 research outputs found
Text to Time Series Representations: Towards Interpretable Predictive Models
No full textTime Series Analysis (TSA) and Natural Language Processing (NLP) are two domains of research that have seen a surge of interest in recent years. NLP focuses mainly on enabling computers to manipulate and generate human language, whereas TSA identifies patterns or components in time-dependent data. Given their different purposes, there has been limited exploration of combining them. In this study, we present an approach to convert text into time series to exploit TSA for exploring text properties and to make NLP approaches interpretable for humans. We formalize our Text to Time Series framework as a feature extraction and aggregation process, proposing a set of different conversion alternatives for each step. We experiment with our approach on several textual datasets, showing the conversion approach’s performance and applying it to the field of interpretable time series classification
Fast, Interpretable and Deterministic Time Series Classification with a Bag-Of-Receptive-Fields
No full textThe current trend in the literature on Time Series Classification is to develop increasingly accurate algorithms by combining multiple models in ensemble hybrids, representing time series in complex and expressive feature spaces, and extracting features from different representations of the same time series. As a consequence of this focus on predictive performance, the best time series classifiers are black-box models, which are not understandable from a human standpoint. Even the approaches that are regarded as interpretable, such as shapelet-based ones, rely on randomization to maintain computational efficiency. This poses challenges for interpretability, as the explanation can change from run to run. Given these limitations, we propose the Bag-Of-Receptive-Field (BORF), a fast, interpretable, and deterministic time series transform. Building upon the classical Bag-Of-Patterns, we bridge the gap between convolutional operators and discretization, enhancing the Symbolic Aggregate Approximation (SAX) with dilation and stride, which can more effectively capture temporal patterns at multiple scales. We propose an algorithmic speedup that reduces the time complexity associated with SAX-based classifiers, allowing the extension of the Bag-Of-Patterns to the more flexible Bag-Of-Receptive-Fields, represented as a sparse multivariate tensor. The empirical results from testing our proposal on more than 150 univariate and multivariate classification datasets demonstrate good accuracy and great computational efficiency compared to traditional SAX-based methods and state-of-the-art time series classifiers, while providing easy-to-understand explanations
Explainable AI for Time Series Classification: A Review, Taxonomy and Research Directions
No full textTime series data is increasingly used in a wide range of fields, and it is often relied on in crucial applications and high-stakes decision-making. For instance, sensors generate time series data to recognize different types of anomalies through automatic decision-making systems. Typically, these systems are realized with machine learning models that achieve top-tier performance on time series classification tasks. Unfortunately, the logic behind their prediction is opaque and hard to understand from a human standpoint. Recently, we observed a consistent increase in the development of explanation methods for time series classification justifying the need to structure and review the field. In this work, we (a) present the first extensive literature review on Explainable AI (XAI) for time series classification, (b) categorize the research field through a taxonomy subdividing the methods into time points-based, subsequences-based and instance-based, and (c) identify open research directions regarding the type of explanations and the evaluation of explanations and interpretability
ICT-driven Innovation in a Sample of European Museums: Mapping the Positioning Through Site-Centered, Site-Uncentered and Border Technologies
No full textThis paper aims to investigate the central role of Innovation and Communication Technologies (ICT) in the European cultural touristic sector. To this aim, the research group proposes a theoretical framework to provide a description and interpretation of use of ICT in a sample of European Museums of the leading capitals, as follow - Paris, Barcelona, London, Amsterdam, Rome, Berlin, Vienna, Stockholm, Lisbon and Helsinki. More specifically, it was conducted quali-quantitative empirical research on the sample, as a second step of a longitudinal analysis developed by the research group, aiming to broaden the previous representation. It was developed an updated theoretical framework structured on 41 variables classified according to visit phases (pre, post and during the visit) and to the provision mechanisms of the technology (on-site or online). The data was collected on a secondary basis from the museums’ websites.
Subsequently, a cluster analysis based on the technologies adopted by the sample was run to create positioning maps. The results show how a logarithmic transformation of the number of visitors and the previously adoption of on-site technologies impact on the willingness to adopt particularly innovative and disruptive technologies from the museums point of view. The positioning maps bring out an important consideration: the Barcelona FC Museum differs
considerably from all other museums of the sample for the adoption of on-site technologies and of highly innovative technologies.
The research also shows that large museums are more likely to adopt online booking systems, while smaller museums tend to prefer adopting fixed technologies that can improve the visiting experience, especially if integrated with their cultural heritage
Appropriate Similarity Measures for Author Cocitation Analysis
No full textWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Explaining Any Time Series Classifier
No full textWe present a method to explain the decisions of black box models for time series classification. The explanation consists of factual and counterfactual shapelet-based rules revealing the reasons for the classification, and of a set of exemplars and counter-exemplars highlighting similarities and differences with the time series under analysis. The proposed method first generates exemplar and counter-exemplar time series in the latent feature space and learns a local latent decision tree classifier. Then, it selects and decodes those respecting the decision rules explaining the decision. Finally, it learns on them a shapelet-tree that reveals the parts of the time series that must, and must not, be contained for getting the returned outcome from the black box. A wide experimentation shows that the proposed method provides faithful, meaningful and interpretable explanations
Geolet: An Interpretable Model for Trajectory Classification
No full textThe large and diverse availability of mobility data enables the development of predictive models capable of recognizing various types of movements. Through a variety of GPS devices, any moving entity, animal, person, or vehicle can generate spatio-temporal trajectories. This data is used to infer migration patterns, manage traffic in large cities, and monitor the spread and impact of diseases, all critical situations that necessitate a thorough understanding of the underlying problem. Researchers, businesses, and governments use mobility data to make decisions that affect people’s lives in many ways, employing accurate but opaque deep learning models that are difficult to interpret from a human standpoint. To address these limitations, we propose Geolet, a human-interpretable machine-learning model for trajectory classification. We use discriminative sub-trajectories extracted from mobility data to turn trajectories into a simplified representation that can be used as input by any machine learning classifier. We test our approach against state-of-the-art competitors on real-world datasets. Geolet outperforms black-box models in terms of accuracy while being orders of magnitude faster than its interpretable competitors
"Closing the R&D Gap, Evaluating the Sources of R&D Spending"
No full textBoth spending and tax policies have been implemented in the United States with the goal of stimulating private sector research and development (R&D). Karier questions whether current R&D policy, especially the research and experimentation tax credit, can contribute to closing the gap between nondefense expenditures on R&D in the United States and such expenditures in other countries, such as Japan and Germany. He also explores possible changes to our current R&D policy to make it more effective.
UNVEILING THE CHALLENGES OF PARTICIPATORY PLANNING IN TERRITORIAL GOVERNMENT. THE CASE STUDY OF FOGGIAWELCOME PROJECT
No full textThe paper examines the participatory planning as a territorial governance approach that involves stakeholders – socio-economic actors with different roles and capacities – in the decision-making process and its impact on shaping ideas for the development and enhancement of a territorial area. In particular, the aim of this study is to highlight the potential impact of stakeholders’ participation in decision-making processes, emphasising their role in shaping the development trajectory and the appreciation of a territory.
To this end, the paper focuses on the case study of FoggiaWelcome, a platform for the promotion and enhancement of the territory of Capitanata (Foggia), delving into the intricate web of relationships, networks, and top-down/bottom-up dynamics that shape it.
Coherently with the participatory approach, the paper explores the dynamics of bottom-up participation and delves into the multifaceted aspects of stakeholder and community involvement, examining how local actors contribute to the decision-making process, fostering a sense of ownership and shared responsibility for the development of their region. In this way, this study aims to analyse Foggia Welcome from a holistic perspective, unravelling the interconnections and synergies that emerge organically from the local level, emerging as an illuminating example providing insights into the interplay of diverse stakeholders, collaborative networks, and community-driven initiatives in redefining the narrative of regional development.
Results emphasize the potential for bottom-up participation to promote innovation, inclusivity, and sustainability in regional development initiatives, providing insight into the dynamics that shape the development narrative, going beyond the traditional bottom-up approach.
The case study is currently being evaluated with a focus on its initial steps. It is hypothesized that it may develop into a more comprehensive platform aligned with the project’s objectives. The discussion emphasizes the platform’s early stage and hints at a forthcoming evolution. The study acknowledges its limitations in assessing a nascent project and foresees potential advancement towards a more developed form. The evaluation considers the platform’s potential for growth and its alignment with the project’s overall goals
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