Abstract
There is a growing trend towards consumer-focused approaches integrating distributed generation resources in the power and energy sector. This, however, adds complexity to community management as new players are introduced. The authors have designed a trustworthy rate (TR) system to address this issue of selecting participants for demand response events based on their previous performance. The aim is to avoid discomfort for consumers and reduce aggregator costs by selecting participants fairly. However, this poses a challenge for new players with a performance history. This study aims to develop a method to assign the TR to new players. For this purpose, the authors used supervised clustering and subgroup discovery to identify the relevant features of the dataset without compromising privacy after employing techniques such as Decision Trees, Random Forest, and Extreme Gradient boosting to assign the appropriate TR to each player. The performance of the methods has been evaluated using metrics such as accuracy, precision, recall, and f1 score. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Abstract

Abstract
Monitoring a system over time is as important as ever with the increasing use of cloud-native software architectures. This paper expands the set of patterns published in a previous paper (Liveness Endpoint, Readiness Endpoint and Synthetic Testing) with two solutions for supporting teams in diagnosing occurring issues — Deployment Tracking and Exception Tracking. These patterns advise tracking relevant events that occur in the system. The Deployment Tracking pattern provides means to limit the sources of an anomaly, and the Exception Tracking pattern makes a specific class of anomalies visible so that a team can act on them. Both patterns help practitioners identify the root cause of an issue, which is instrumental in fixing it. They can help even less experienced professionals to improve monitoring processes, and reduce the mean time to resolve problems with their application. These patterns draw on documented industry best practices and existing tools. In order to help the reader find other patterns that supplement the ones suggested in this study, relations to already-existing monitoring patterns are also examined.

Abstract
Optical Coherence Tomography (OCT) is the major diagnostic tool for the leading cause of blindness in developed countries: Diabetic Macular Edema (DME). Depending on the type of fluid accumulations, different treatments are needed. In particular, Cystoid Macular Edemas (CMEs) represent the most severe scenario, while Diffuse Retinal Thickening (DRT) is an early indicator of the disease but a challenging scenario to detect. While methodologies exist, their explanatory power is limited to the input sample itself. However, due to the complexity of these accumulations, this may not be enough for a clinician to assess the validity of the classification. Thus, in this work, we propose a novel approach based on multi-prototype networks with vision transformers to obtain an example-based explainable classification. Our proposal achieved robust results in two representative OCT devices, with a mean accuracy of 0.9099 ± 0.0083 and 0.8582 ± 0.0126 for CME and DRT-type fluid accumulations, respectively.

Abstract

Abstract