Abstract
The Natural Language Processing (NLP) community has witnessed huge improvements in the last years. However, most achievements are evaluated on benchmarked curated corpora, with little attention devoted to user-generated content and less-resourced languages. Despite the fact that recent approaches target the development of multi-lingual tools and models, they still underperform in languages such as Portuguese, for which linguistic resources do not abound. This paper exposes a set of challenges encountered when dealing with a real-world complex NLP problem, based on user-generated complaint data in Portuguese. This case study meets the needs of a country-wide governmental institution responsible for food safety and economic surveillance, and its responsibilities in handling a high number of citizen complaints. Beyond looking at the problem from an exclusively academic point of view, we adopt application-level concerns when analyzing the progress obtained through different techniques, including the need to obtain explainable decision support. We discuss modeling choices and provide useful insights for researchers working on similar problems or data.

Abstract
Bin picking is a challenging problem that involves using a robotic manipulator to remove, one-by-one, a set of objects randomly stacked in a container. In order to provide ground truth data for evaluating heuristic or machine learning perception systems, this paper proposes using simulation to create bin picking environments in which a procedural generation method builds entangled tubes that can have curvatures throughout their length. The output of the simulation is an annotated point cloud, generated by a virtual 3D depth camera, in which the tubes are assigned with unique colors. A general metric based on micro-recall is proposed to compare the accuracy of point cloud annotations with the ground truth. The synthetic data is representative of a high quality 3D scanner, given that the performance of a tube modeling system when given 640 simulated point clouds was similar to the results achieved with real sensor data. Therefore, simulation is a promising technique for the automated evaluation of solutions for bin picking tasks.

Abstract
This work presents a framework for a new type of meta-game balance AI Competition based on Pokemon. Pokemon battles can be viewed as adversarial games played by AIs. Around these games, there is also a meta-game: which Pokemon to include in a team for battles, which moves to pick for every Pokemon in the team, etc. This meta-game is itself a game with a set of rules that govern which Pokemon and which moves are available in the roster that can be selected from, or which attributes (health points, damage, etc.) a Pokemon or moves should have. The aim of the framework is to facilitate competitions in creating the most balanced meta-game possible; one where there is a large variety of Pokemon and moves to choose from, and many possible combinations that are effective. AI agents could assist human designers in achieving strategically expressive meta-games, and this type of benchmark could incentivize game designers and researchers alike to advance knowledge on this type of domain.

Abstract

Abstract
The emergence of technologies capable of producing real-time data opened new horizons to planning and optimising vehicle routes. Dynamic vehicle routing problems (DVRPs) use real-time information to dynamically calculate the most optimised set of routes. The typical approach is to initially calculate the vehicle routes and dynamically revise them in real-time. This work uses the case study of ASAE, a Portuguese administrative authority specialising in food safety and economic surveillance. The dynamic properties of ASAE's operational environment are studied, and a solution is proposed to review and efficiently modify the precalculated plan. We propose a weighted utility function based on three aspects: the summed utility of the inspections, the similarity between solutions, and the arrival time. A Disruption Generator generates disruptions on the inspection routes: travel and inspection times, vehicle and inspection breakdowns, utility changes, and unexpected or emerging inspections. We compare the performance of four meta-heuristics: Hill-Climbing (HC), Simulated Annealing (SA), Tabu-Search (TS) and Large neighbourhood Search (LNS). The HC algorithm has the fastest convergence, while SA takes longer to solve the test instances. LNS was the method with higher solution quality, while HC provided solutions with lower utility.

Abstract
The human brain can quickly become overwhelmed by the amounts of data computers can process. Consequently, data abstraction is necessary for a user to grasp information and identify valuable patterns. Data is usually abstracted in a pictorial or graphical format. Nowadays, users demand more personalization from the systems they use. This work proposes a user-centered framework that aims to ease creating visualizations for the developers of a platform while offering the end-user a highly customizable experience. The conceptualized solution was prototyped and tested to ensure the information about the data is transmitted to the user in a quick and effective manner. The results of a user study showed that users are pleased with the usability of the prototype and prove that they desire control over the configuration of their visualizations. This work not only confirmed the usefulness of previously explored personalization options for visual representations, but also explored promising new personalization options. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.