A Citizen Science Approach for Analyzing Social Media With Crowdsourcing

Social media have the potential to provide timely information about emergency situations and sudden events. However, finding relevant information among the millions of posts being added every day can be difficult, and in current approaches developing an automatic data analysis project requires time and technical skills. This work presents a new approach for the analysis of social media posts, based on configurable automatic classification combined with Citizen Science methodologies. The process is facilitated by a set of flexible, automatic and open-source data processing tools called the Citizen Science Solution Kit. The kit provides a comprehensive set of tools that can be used and personalized in different situations, particularly during natural emergencies, starting from images and text contained in the posts. The tools can be employed by citizen scientists for filtering, classifying, and geolocating the content with a human-in-the-loop approach to support the data analyst, including feedback and suggestions on how to configure the automated tools, and techniques to gather inputs from citizens. Using flooding scenario as a guiding example, this paper illustrates the structure and functioning of the different tools proposed to support citizens scientists in their projects, and a methodological approach to their use. The process is then validated by discussing three case studies based on the Albania earthquake of 2019, the Covid-19 pandemic, and the Thailand floods of 2021. The results suggest that a flexible approach to tools composition and configuration can support a timely setup of an analysis project by citizen scientists, especially in case of emergencies in unexpected locations.ISSN:2169-353

Understanding Dubious Future Problems

[EN]Being able to predict the performance of a Case-Based Reasoning system against a set of future problems would provide invaluable information for design and maintenance of the system. Thus, we could carry out the needed design changes and maintenance tasks to improve future performance in a proactive fashion. This paper proposes a novel method for identifying regions in a case base where the system gives low confidence solutions to possible future problems. Experimentation is provided for RoboSoccer domain and we argue how encountered regions of dubiosity help us to analyse the case base and the reasoning mechanisms of the given Case-Based Reasoning system.Peer reviewe

Fast anytime retrieval with confidence in large-scale temporal case bases

This work is about speeding up retrieval in Case-Based Reasoning (CBR) for large-scale case bases (CBs) comprised of temporally related cases in metric spaces. A typical example is a CB of electronic health records where consecutive sessions of a patient forms a sequence of related cases. k-Nearest Neighbors (kNN) search is a widely used algorithm in CBR retrieval. However, brute-force kNN is impossible for large CBs. As a contribution to efforts for speeding up kNN search, we introduce an anytime kNN search methodology and algorithm. Anytime Lazy kNN finds exact kNNs when allowed to run to completion with remarkable gain in execution time by avoiding unnecessary neighbor assessments. For applications where the gain in exact kNN search may not suffice, it can be interrupted earlier and it returns best-so-far kNNs together with a confidence value attached to each neighbor. We describe the algorithm and methodology to construct a probabilistic model that we use both to estimate confidence upon interruption and to automatize the interruption at desired confidence thresholds. We present the results of experiments conducted with publicly available datasets. The results show superior gains compared to brute-force search. We reach to an average gain of 87.18% with 0.98 confidence and to 96.84% with 0.70 confidence.This work has been funded by the project Playing and Singing for the Recovering Brain: Efficacy of Enriched Social-Motivational Musical Interventions in Stroke Rehabilitation (Play&Sing), Spain, 201729.31, Fundació La Marató de TV3, Spain; and, by the project Innobrain, Spain, COMRDI-151-0017 (RIS3CAT comunitats), and Feder, Spain funds

Using Introspective Reasoning to Improve CBR System Performance

[EN]When AI technologies are applied to real-world problems, it is often difficult for developers to anticipate all the knowledge needed. Previous research has shown that introspective reasoning can be a useful tool for helping to address this problem in case-based reasoning systems, by enabling them to augment their routine learning of cases with learning to make better use of their cases, as problem-solving experience reveals deficiencies in their reasoning process. In this paper we present a new introspective model for autonomously improving the performance of a CBR system by reasoning about system problem solving failures. We illustrate its benefits with experimental results from tests in an industrial design application.Peer reviewe

Design of an AI Platform to Support Home-Based Self-Training Music Interventions for Chronic Stroke Patients

In the Play&Sing project, we are developing an AI platform to support home-based self-training interventions for chronic stroke patients. A large percentage of patients suffering from this disease show motor deficits that clearly hinder their daily activities and diminish their quality of life. In this project we are proposing and testing a new Music Supported Therapy (MST) to induce upper limb motor recovery.With the help of a tablet-based application and a small musical keyboard, we are developing an AI platform to support home-based MST. Specifically, the role of AI algorithms is to support therapists and to boost user engagement by personalizing the interventions according to patient needs and preferences. AI algorithms will provide the therapists with hindsight and foresight tools. In the proposed MST, patients are performing 30 training sessions of 45 minutes with a frequency of 3 sessions per week. In this paper we present our platform and preliminary experiments conducted at a pilot phase

Design of an AI Platform to Support Home-Based Self-Training Music Interventions for Chronic Stroke Patients.

[EN]In the Play&Sing project, we are developing an AI platform to support home-based self-training interventions for chronic stroke patients. A large percentage of patients suffering from this disease show motor deficits that clearly hinder their daily activities and diminish their quality of life. In this project we are proposing and testing a new Music Supported Therapy (MST) to induce upper limb motor recovery.With the help of a tablet-based application and a small musical keyboard, we are developing an AI platform to support home-based MST. Specifically, the role of AI algorithms is to support therapists and to boost user engagement by personalizing the interventions according to patient needs and preferences. AI algorithms will provide the therapists with hindsight and foresight tools. In the proposed MST, patients are performing 30 training sessions of 45 minutes with a frequency of 3 sessions per week. In this paper we present our platform and preliminary experiments conducted at a pilot phase.Peer reviewe