Abstract

Abstract
The widespread adoption of distributed energy resources (DER) is creating an opportunity for aggregators to transform DER flexibility into electricity market services. In a scenario of high DER integration, aggregators will need to coordinate the optimisation of DER with the distribution system operator (DSO) in order to avoid congestion and voltage incursions in the distribution networks. This coordination task is notably complex since both network and DER operation are impacted by multiple sources of uncertainty. To address these challenges, this paper proposes a new bidding strategy for aggregators of prosumers to make robust network-secure bidding decisions in day-ahead energy and reserve markets. The bidding strategy computes robust network-secure bids without jeopardising the data privacy of aggregators and the DSO. The data privacy is preserved by using the alternating direction method of multipliers (ADMM) to decompose a stochastic network-secure bidding problem into bidding and network subproblems and solve them separately and in parallel. The uncertainty of the prosumers is incorporated in the bidding problem through scenarios of load, renewable generation, and DER preferences. Our experiments show that the proposed bidding strategy computes robust bids against distribution network problems, outperforming deterministic and stochastic state-of-the-art bidding strategies in terms of cost and network observability.

Abstract
Modelling complex information systems often entails the need for dealing with scenarios of inconsistency in which several requirements either reinforce or contradict each other. In this kind of scenarios, arising e.g. in knowledge representation, simulation of biological systems, or quantum computation, inconsistency has to be addressed in a precise and controlled way. This paper generalises Belnap-Dunn four-valued logic, introducing paraconsistent transition systems (PTS), endowed with positive and negative accessibility relations, and a metric space over the lattice of truth values, and their modal logic.

Abstract
When developing mobile applications, developers often have to decide when to acquire and when to release resources. This leads to resource leaks, a kind of bug where a resource is acquired but never released. This is a common problem in Android applications that can degrade energy efficiency and, in some cases, can cause resources to not function properly. In this paper, we present an extension of EcoAndroid, an Android Studio plugin that improves the energy efficiency of Android applications, with an inter-procedural static analysis that detects resource leaks. Our analysis is implemented using Soot, FlowDroid, and Heros, which provide a static-analysis environment capable of processing Android applications and performing inter-procedural analysis with the IFDS framework. It currently supports the detection of leaks related to four Android resources: Cursor, SQLite-Database, Wakelock, and Camera. We evaluated our tool with the DroidLeaks benchmark and compared it with 8 other resource leak detectors. We obtained a precision of 72.5% and a recall of 83.2%. Our tool was able to uncover 191 previously unidentified leaks in this benchmark. These results show that our analysis can help developers identify resource leaks.

Abstract
In this paper we present findings from a bibliometric evaluation of scientific publications on human-AI systems, indexed in the Dimensions database over the past five years (2018 to 2022). The study maps the research landscape in this burgeoning area, as it relates to the topic of collaboration. To this end, we assessed publication and citation counts over time, authorship-level indicators, and keyword occurrence frequency. We also examined funding information as an indicator of research priorities, alongside usage-based statistics and alternative metrics such as social media mentions, recommendations, and reads. Our preliminary findings highlight a significant focus on aspects like trust, explainability, transparency, and autonomy in highly complex scenarios through the use of generative models and hybrid interaction techniques. The results also reveal a growth in the number of publications and funding grants, although a certain lack of maturity is observable in terms of citation patterns and coherence of thematic clusters. © 2022 IEEE.

Abstract
Industry 4.0 has been changing and improving the manufacturing processes. To embrace these changes, factories must keep up to date with all the new emerging technologies. In the automotive industry, the growing demand for customization and constant car model changes leads to an inevitable grow of complexity of the final product quality inspection process. In the project INDTECH 4.0, smart technologies are being explored in an automotive factory assembly line to automate the vehicle quality control, which still relies on human inspection based on paper conformity checklists. This paper proposes an automated inspection process based on computer vision to assist operators in the conformity assessment of informative labels affixed inside the engine compartment of the car. Two of the most recent object detection algorithms: YOLOv5 and YOLOX are evaluated for the identification of labels in the images. Our results show high mean average precision on both algorithms (98%), which overall, tells us that both algorithms showed good performances and have potential to be implemented in the shop floor to support the vehicle quality control.