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Article ; Online: Human-in-the-Loop: Visual Analytics for Building Models Recognising Behavioural Patterns in Time Series.

Andrienko, Natalia / Andrienko, Gennady / Artikis, Alexander / Mantenoglou, Periklis / Rinzivillo, Salvatore

IEEE computer graphics and applications

2024 Volume PP

Abstract: Results of automated detection of complex patterns in temporal data, such as trajectories of moving objects, may be not good enough due to the use of strict pattern specifications derived from imprecise domain concepts. To address this challenge, we ... ...

Abstract	Results of automated detection of complex patterns in temporal data, such as trajectories of moving objects, may be not good enough due to the use of strict pattern specifications derived from imprecise domain concepts. To address this challenge, we propose a novel visual analytics approach that combines expert knowledge and automated pattern detection results to construct features that effectively distinguish patterns of interest from other types of behaviour. These features are then used to create interactive visualisations enabling a human analyst to generate labelled examples for building a feature-based pattern classifier. We evaluate our approach through a case study focused on detecting trawling activities in fishing vessel trajectories, demonstrating significant improvements in pattern recognition by leveraging domain knowledge and incorporating human reasoning and feedback. Our contribution is a novel framework that integrates human expertise and analytical reasoning with ML or AI techniques, advancing the field of data analytics.
Language	English
Publishing date	2024-03-20
Publishing country	United States
Document type	Journal Article
ISSN	1558-1756
ISSN (online)	1558-1756
DOI	10.1109/MCG.2024.3379851
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article: Towards Transparent Healthcare: Advancing Local Explanation Methods in Explainable Artificial Intelligence.

Metta, Carlo / Beretta, Andrea / Pellungrini, Roberto / Rinzivillo, Salvatore / Giannotti, Fosca

Bioengineering (Basel, Switzerland)

2024 Volume 11, Issue 4

Abstract: This paper focuses on the use of local Explainable Artificial Intelligence (XAI) methods, particularly the Local Rule-Based Explanations (LORE) technique, within healthcare and medical settings. It emphasizes the critical role of interpretability and ... ...

Abstract	This paper focuses on the use of local Explainable Artificial Intelligence (XAI) methods, particularly the Local Rule-Based Explanations (LORE) technique, within healthcare and medical settings. It emphasizes the critical role of interpretability and transparency in AI systems for diagnosing diseases, predicting patient outcomes, and creating personalized treatment plans. While acknowledging the complexities and inherent trade-offs between interpretability and model performance, our work underscores the significance of local XAI methods in enhancing decision-making processes in healthcare. By providing granular, case-specific insights, local XAI methods like LORE enhance physicians' and patients' understanding of machine learning models and their outcome. Our paper reviews significant contributions to local XAI in healthcare, highlighting its potential to improve clinical decision making, ensure fairness, and comply with regulatory standards.
Language	English
Publishing date	2024-04-12
Publishing country	Switzerland
Document type	Journal Article ; Review
ZDB-ID	2746191-9
ISSN	2306-5354
ISSN	2306-5354
DOI	10.3390/bioengineering11040369
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article: Advancing Dermatological Diagnostics: Interpretable AI for Enhanced Skin Lesion Classification.

Metta, Carlo / Beretta, Andrea / Guidotti, Riccardo / Yin, Yuan / Gallinari, Patrick / Rinzivillo, Salvatore / Giannotti, Fosca

Diagnostics (Basel, Switzerland)

2024 Volume 14, Issue 7

Abstract: A crucial challenge in critical settings like medical diagnosis is making deep learning models used in decision-making systems interpretable. Efforts in Explainable Artificial Intelligence (XAI) are underway to address this challenge. Yet, many XAI ... ...

Abstract	A crucial challenge in critical settings like medical diagnosis is making deep learning models used in decision-making systems interpretable. Efforts in Explainable Artificial Intelligence (XAI) are underway to address this challenge. Yet, many XAI methods are evaluated on broad classifiers and fail to address complex, real-world issues, such as medical diagnosis. In our study, we focus on enhancing user trust and confidence in automated AI decision-making systems, particularly for diagnosing skin lesions, by tailoring an XAI method to explain an AI model's ability to identify various skin lesion types. We generate explanations using synthetic images of skin lesions as examples and counterexamples, offering a method for practitioners to pinpoint the critical features influencing the classification outcome. A validation survey involving domain experts, novices, and laypersons has demonstrated that explanations increase trust and confidence in the automated decision system. Furthermore, our exploration of the model's latent space reveals clear separations among the most common skin lesion classes, a distinction that likely arises from the unique characteristics of each class and could assist in correcting frequent misdiagnoses by human professionals.
Language	English
Publishing date	2024-04-02
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2662336-5
ISSN	2075-4418
ISSN	2075-4418
DOI	10.3390/diagnostics14070753
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Book ; Online: Exemplars and Counterexemplars Explanations for Image Classifiers, Targeting Skin Lesion Labeling

Metta, Carlo / Guidotti, Riccardo / Yin, Yuan / Gallinari, Patrick / Rinzivillo, Salvatore

2023

Abstract: Explainable AI consists in developing mechanisms allowing for an interaction between decision systems and humans by making the decisions of the formers understandable. This is particularly important in sensitive contexts like in the medical domain. We ... ...

Abstract	Explainable AI consists in developing mechanisms allowing for an interaction between decision systems and humans by making the decisions of the formers understandable. This is particularly important in sensitive contexts like in the medical domain. We propose a use case study, for skin lesion diagnosis, illustrating how it is possible to provide the practitioner with explanations on the decisions of a state of the art deep neural network classifier trained to characterize skin lesions from examples. Our framework consists of a trained classifier onto which an explanation module operates. The latter is able to offer the practitioner exemplars and counterexemplars for the classification diagnosis thus allowing the physician to interact with the automatic diagnosis system. The exemplars are generated via an adversarial autoencoder. We illustrate the behavior of the system on representative examples. Comment: arXiv admin note: text overlap with arXiv:2111.11863
Keywords	Electrical Engineering and Systems Science - Image and Video Processing ; Computer Science - Artificial Intelligence ; Computer Science - Computer Vision and Pattern Recognition ; Computer Science - Machine Learning
Subject code	006
Publishing date	2023-01-18
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Predicting seasonal influenza using supermarket retail records.

Miliou, Ioanna / Xiong, Xinyue / Rinzivillo, Salvatore / Zhang, Qian / Rossetti, Giulio / Giannotti, Fosca / Pedreschi, Dino / Vespignani, Alessandro

PLoS computational biology

2021 Volume 17, Issue 7, Page(s) e1009087

Abstract: Increased availability of epidemiological data, novel digital data streams, and the rise of powerful machine learning approaches have generated a surge of research activity on real-time epidemic forecast systems. In this paper, we propose the use of a ... ...

Abstract	Increased availability of epidemiological data, novel digital data streams, and the rise of powerful machine learning approaches have generated a surge of research activity on real-time epidemic forecast systems. In this paper, we propose the use of a novel data source, namely retail market data to improve seasonal influenza forecasting. Specifically, we consider supermarket retail data as a proxy signal for influenza, through the identification of sentinel baskets, i.e., products bought together by a population of selected customers. We develop a nowcasting and forecasting framework that provides estimates for influenza incidence in Italy up to 4 weeks ahead. We make use of the Support Vector Regression (SVR) model to produce the predictions of seasonal flu incidence. Our predictions outperform both a baseline autoregressive model and a second baseline based on product purchases. The results show quantitatively the value of incorporating retail market data in forecasting models, acting as a proxy that can be used for the real-time analysis of epidemics.
MeSH term(s)	Computational Biology ; Consumer Behavior/statistics & numerical data ; Humans ; Incidence ; Influenza, Human/epidemiology ; Italy/epidemiology ; Seasons ; Supermarkets
Language	English
Publishing date	2021-07-12
Publishing country	United States
Document type	Journal Article ; Research Support, N.I.H., Extramural
ZDB-ID	2193340-6
ISSN	1553-7358 ; 1553-734X
ISSN (online)	1553-7358
ISSN	1553-734X
DOI	10.1371/journal.pcbi.1009087
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article: Exploiting Spatial Abstraction in Predictive Analytics of Vehicle Traffic

Andrienko, Natalia / Andrienko, Gennady / Rinzivillo, Salvatore

ISPRS international journal of geo-information. 2015 Apr. 15, v. 4, no. 2

2015

Abstract: By applying visual analytics techniques to vehicle traffic data, we found a way to visualize and study the relationships between the traffic intensity and movement speed on links of a spatially abstracted transportation network. We observed that the ... ...

Abstract	By applying visual analytics techniques to vehicle traffic data, we found a way to visualize and study the relationships between the traffic intensity and movement speed on links of a spatially abstracted transportation network. We observed that the traffic intensities and speeds in an abstracted network are interrelated in the same way as they are in a detailed street network at the level of street segments. We developed interactive visual interfaces that support representing these interdependencies by mathematical models. To test the possibility of utilizing them for performing traffic simulations on the basis of abstracted transportation networks, we devised a prototypical simulation algorithm employing these dependency models. The algorithm is embedded in an interactive visual environment for defining traffic scenarios, running simulations, and exploring their results. Our research demonstrates a principal possibility of performing traffic simulations on the basis of spatially abstracted transportation networks using dependency models derived from real traffic data. This possibility needs to be comprehensively investigated and tested in collaboration with transportation domain specialists.
Keywords	algorithms ; mathematical models ; spatial data ; traffic
Language	English
Dates of publication	2015-0415
Size	p. 591-606.
Publishing place	Multidisciplinary Digital Publishing Institute
Document type	Article
ZDB-ID	2655790-3
ISSN	2220-9964
ISSN	2220-9964
DOI	10.3390/ijgi4020591
Database	NAL-Catalogue (AGRICOLA)

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Book ; Online: Predicting seasonal influenza using supermarket retail records

Miliou, Ioanna / Xiong, Xinyue / Rinzivillo, Salvatore / Zhang, Qian / Rossetti, Giulio / Giannotti, Fosca / Pedreschi, Dino / Vespignani, Alessandro

2020

Abstract	Increased availability of epidemiological data, novel digital data streams, and the rise of powerful machine learning approaches have generated a surge of research activity on real-time epidemic forecast systems. In this paper, we propose the use of a novel data source, namely retail market data to improve seasonal influenza forecasting. Specifically, we consider supermarket retail data as a proxy signal for influenza, through the identification of sentinel baskets, i.e., products bought together by a population of selected customers. We develop a nowcasting and forecasting framework that provides estimates for influenza incidence in Italy up to 4 weeks ahead. We make use of the Support Vector Regression (SVR) model to produce the predictions of seasonal flu incidence. Our predictions outperform both a baseline autoregressive model and a second baseline based on product purchases. The results show quantitatively the value of incorporating retail market data in forecasting models, acting as a proxy that can be used for the real-time analysis of epidemics. Comment: 17 pages, 2 figures, 4 tables (1 in appendix), 1 algorithm, submitted to PLOS Computational Biology
Keywords	Computer Science - Social and Information Networks ; Computer Science - Artificial Intelligence ; Computer Science - Machine Learning
Subject code	006
Publishing date	2020-12-08
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Scalable analysis of movement data for extracting and exploring significant places.

Andrienko, Gennady / Andrienko, Natalia / Hurter, Christophe / Rinzivillo, Salvatore / Wrobel, Stefan

IEEE transactions on visualization and computer graphics

2013 Volume 19, Issue 7, Page(s) 1078–1094

Abstract: Place-oriented analysis of movement data, i.e., recorded tracks of moving objects, includes finding places of interest in which certain types of movement events occur repeatedly and investigating the temporal distribution of event occurrences in these ... ...

Abstract	Place-oriented analysis of movement data, i.e., recorded tracks of moving objects, includes finding places of interest in which certain types of movement events occur repeatedly and investigating the temporal distribution of event occurrences in these places and, possibly, other characteristics of the places and links between them. For this class of problems, we propose a visual analytics procedure consisting of four major steps: 1) event extraction from trajectories; 2) extraction of relevant places based on event clustering; 3) spatiotemporal aggregation of events or trajectories; 4) analysis of the aggregated data. All steps can be fulfilled in a scalable way with respect to the amount of the data under analysis; therefore, the procedure is not limited by the size of the computer's RAM and can be applied to very large data sets. We demonstrate the use of the procedure by example of two real-world problems requiring analysis at different spatial scales.
Language	English
Publishing date	2013-07
Publishing country	United States
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ISSN	1941-0506
ISSN (online)	1941-0506
DOI	10.1109/TVCG.2012.311
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Article ; Online: Returners and explorers dichotomy in human mobility.

Pappalardo, Luca / Simini, Filippo / Rinzivillo, Salvatore / Pedreschi, Dino / Giannotti, Fosca / Barabási, Albert-László

Nature communications

2015 Volume 6, Page(s) 8166

Abstract: The availability of massive digital traces of human whereabouts has offered a series of novel insights on the quantitative patterns characterizing human mobility. In particular, numerous recent studies have lead to an unexpected consensus: the ... ...

Abstract	The availability of massive digital traces of human whereabouts has offered a series of novel insights on the quantitative patterns characterizing human mobility. In particular, numerous recent studies have lead to an unexpected consensus: the considerable variability in the characteristic travelled distance of individuals coexists with a high degree of predictability of their future locations. Here we shed light on this surprising coexistence by systematically investigating the impact of recurrent mobility on the characteristic distance travelled by individuals. Using both mobile phone and GPS data, we discover the existence of two distinct classes of individuals: returners and explorers. As existing models of human mobility cannot explain the existence of these two classes, we develop more realistic models able to capture the empirical findings. Finally, we show that returners and explorers play a distinct quantifiable role in spreading phenomena and that a correlation exists between their mobility patterns and social interactions.
MeSH term(s)	Cell Phone ; Exploratory Behavior ; Geographic Information Systems ; Humans ; Interpersonal Relations ; Models, Theoretical ; Social Behavior ; Travel
Language	English
Publishing date	2015-09-08
Publishing country	England
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ISSN	2041-1723
ISSN (online)	2041-1723
DOI	10.1038/ncomms9166
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article: Mobile phone data analytics against the COVID-19 epidemics in Italy: flow diversity and local job markets during the national lockdown

Bonato, Pietro / Cintia, Paolo / Fabbri, Francesco / Fadda, Daniele / Giannotti, Fosca / Lopalco, Pier Luigi / Mazzilli, Sara / Nanni, Mirco / Pappalardo, Luca / Pedreschi, Dino / Penone, Francesco / Rinzivillo, Salvatore / Rossetti, Giulio / Savarese, Marcello / Tavoschi, Lara

Abstract: Understanding collective mobility patterns is crucial to plan the restart of production and economic activities, which are currently put in stand-by to fight the diffusion of the epidemics. In this report, we use mobile phone data to infer the movements ... ...

Abstract	Understanding collective mobility patterns is crucial to plan the restart of production and economic activities, which are currently put in stand-by to fight the diffusion of the epidemics. In this report, we use mobile phone data to infer the movements of people between Italian provinces and municipalities, and we analyze the incoming, outcoming and internal mobility flows before and during the national lockdown (March 9th, 2020) and after the closure of non-necessary productive and economic activities (March 23th, 2020). The population flow across provinces and municipalities enable for the modelling of a risk index tailored for the mobility of each municipality or province. Such an index would be a useful indicator to drive counter-measures in reaction to a sudden reactivation of the epidemics. Mobile phone data, even when aggregated to preserve the privacy of individuals, are a useful data source to track the evolution in time of human mobility, hence allowing for monitoring the effectiveness of control measures such as physical distancing. We address the following analytical questions: How does the mobility structure of a territory change? Do incoming and outcoming flows become more predictable during the lockdown, and what are the differences between weekdays and weekends? Can we detect proper local job markets based on human mobility flows, to eventually shape the borders of a local outbreak?
Keywords	covid19
Publisher	ArXiv
Document type	Article
Database	COVID19

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