In this article we propose a conceptual framework to study ensembles of conformal predictors (CP), that we call Ensemble Predictors (EP). Our approach is inspired by the application of imprecise probabilities in information fusion. Based on the proposed framework, we study, for the first time in the literature, the theoretical properties of CP ensembles in a general setting, by focusing on simple and commonly used possibilistic combination rules. We also illustrate the applicability of the proposed methods in the setting of multivariate time-series classification, showing that these methods provide better performance (in terms of both robustness, conservativeness, accuracy and running time) than both standard classification algorithms and other combination rules proposed in the literature, on a large set of benchmarks from the UCR time series archive.

In this paper, we study human-AI collaboration protocols, a design-oriented construct aimed at establishing and evaluating how humans and AI can collaborate in cognitive tasks. We applied this construct in two user studies involving 12 specialist radiologists (the knee MRI study) and 44 ECG readers of varying expertise (the ECG study), who evaluated 240 and 20 cases, respectively, in different collaboration configurations. We confirm the utility of AI support but find that XAI can be associated with a "white-box paradox", producing a null or detrimental effect. We also find that the order of presentation matters: AI-first protocols are associated with higher diagnostic accuracy than human-first protocols, and with higher accuracy than both humans and AI alone. Our findings identify the best conditions for AI to augment human diagnostic skills, rather than trigger dysfunctional responses and cognitive biases that can undermine decision effectiveness.

In recent times, infrastructure-free indoor positioning has been an important topic of research. Many of the proposed systems are based on pedestrian dead reckoning, thus relying on estimating the heading of the pedestrian. While many studies successfully address the problem of estimating the heading of the device, current approaches have the limitation of requiring the device to be aligned with the pedestrian. To address this problem, we propose an algorithm for estimating the misalignment between the pedestrian and the device by evaluating the acceleration data fit a simplified gait model in each direction. Contrary to similar algorithms, the proposal in this paper does not require a previously trained model nor the detection of steps, and can be implemented using only acceleration data. Furthermore, our experimental results show a significant improvement over the current state of the art.

When a comparison between time series is required, measurement functions provide meaningful scores to characterize similarity between sequences. Quite often, time series appear warped in time, i.e, although they may exhibit amplitude and shape similarity, they appear dephased in time. The most common algorithm to overcome this challenge is the Dynamic Time Warping, which aligns each sequence prior establishing distance measurements. However, Dynamic Time Warping takes only into account amplitude similarity. A distance which characterizes the degree of time warping between two sequences can deliver new insights for applications where the timing factor is essential, such well-defined movements during sports or rehabilitation exercises. We propose a novel measurement called Time Alignment Measurement, which delivers similarity information on the temporal domain. We demonstrate the potential of our approach in measuring performance of time series alignment methodologies and in the characterization of synthetic and real time series data acquired during human movement.

As sensor-rich mobile devices became a commodity, more opportunities appeared for the creation of location-aware services. While GPS is a well established solution for outdoor localization, there is still no standard solution for localization indoors. This paper presents a novel accurate indoor positioning mechanism that is meant to run in common smartphones to be a readily and widely available solution. The system is based on multiple gait-model based filtering techniques for accurate movement quantification in combination with an advanced fused positioning mechanism that leverages sequences of opportunistic observations towards an accurate localization process. Magnetic field fluctuations, Wi-Fi readings and movement data are incrementally matched with a feature spot map containing multi-dimensional spatially-related features that characterize the building. A novel and convenient way of mapping the architectural and environmental properties of buildings is also introduced, which avoids the burden normally associated with the process. The system has been evaluated by multiple users in open and crowded spaces where overall median localization errors between 1.11 m and 1.68 m were obtained. While the reported errors are already satisfactory in the context of indoor localization, improvements may be readily achieved through the inclusion of additional reference features. High accuracy performance coupled with an opportunistic and infrastructure-free approach creates a very desirable solution for the indoor localization market doge.