Abstract
The recent proliferation of so called fake news content, assisted by the widespread use of social media platforms and with serious real-world impacts, makes it imperative to find ways to mitigate this problem. In this paper we propose a machine learning-based approach to tackle it by automatically identifying tweets associated with questionable content, using newly-collected data from Twitter about the 2020 U.S. presidential election. To create a sizable annotated data set, we use an automatic labeling process based on the factual reporting level of links contained in tweets, as classified by human experts. We derive relevant features from that data and investigate the specific contribution of features derived from named entity and emotion recognition techniques, including a novel approach using sequences of prevalent emotions. We conclude the paper by evaluating and comparing the performance of several machine learning models on different test sets, and show they are applicable to addressing the issue of fake news dissemination.

Abstract
Background: This study aims to evaluate food waste and menu quality in two canteens (A and B) from a Portuguese public university in order to identify challenges and opportunities to improve the food service. Methods: Food waste included the analysis of two canteens over 5 consecutive days by selective aggregate weighing. A qualitative evaluation of a 5-week menu cycle related to lunches was performed through the Qualitative Evaluation of Menus (AQE-d) method. Results: Both menus have "satisfactory " evaluations and lower adequacy to the dietary guidelines in criteria A, which evaluates general items from the dish, and in criteria B, which evaluates meat, fish and eggs. The calculated mean of food waste in both canteens exceeded the acceptable limit of 10%, except for the vegetarian (7.5%) dish in canteen A. The biggest waste was found in the vegetarian dish (16.8%) in canteen A. In meat dishes the conduit presents more waste (17.0%) than in fish and vegetarian dishes. Among these, the vegetables were the most wasted (25.3% and 27.9%, respectively). Conclusion: This work presents some insights to future interventions in the direction of a healthier and more sustainable foodservice.

Abstract
The industry 4.0 revolution provides the machines with a sen-sory and communicational capacity, which allows them to mo-nitor and collect large amounts of information. This kind of data have an impact on planning, maintenance, and management of production, enabling real time reaction, efficiency increase, and the development of predictive and process improvement models. The most recent machines are prepared to communicate with the existing monitoring systems, however, many (around 60%) do not. The objective of this work is to present the proposal of a sys-tem for remote monitoring of equipment in real time that meets the requirements of low cost, simplicity, and flexibility. The system monitors the equipment in a simple and agile way, regardless of its sophistication, installation constraints and company resources. A prototype of a system was developed and tested both labo-ratory conditions and a productive environment. The proposed ar-chitecture of the system comprises of a sensor that transmits the machine's signal wirelessly to a gateway which is responsible of collecting all surrounding signals and send it to the cloud. During the testing and assessment of the tools, the results validated the developed prototype. As main result, the proposed solution offers to the industrial market a new low-cost monitoring system based in mature and tested technology laid upon flexible and scalable solutions.

Abstract
The angular size of the broad line region (BLR) of the nearby active galactic nucleus NGC 3783 has been spatially resolved by recent observations with VLTI/GRAVITY. A reverberation mapping (RM) campaign has also recently obtained high quality light curves and measured the linear size of the BLR in a way that is complementary to the GRAVITY measurement. The size and kinematics of the BLR can be better constrained by a joint analysis that combines both GRAVITY and RM data. This, in turn, allows us to obtain the mass of the supermassive black hole in NGC 3783 with an accuracy that is about a factor of two better than that inferred from GRAVITY data alone. We derive M-BH = 2.54(-0.72)(+0.90) x 10(7) M-circle dot. Finally, and perhaps most notably, we are able to measure a geometric distance to NGC 3783 of 39.9(-11.9)(+14.5) Mpc. We are able to test the robustness of the BLR-based geometric distance with measurements based on the Tully-Fisher relation and other indirect methods. We find the geometric distance is consistent with other methods within their scatter. We explore the potential of BLR-based geometric distances to directly constrain the Hubble constant, H-0, and identify differential phase uncertainties as the current dominant limitation to the H-0 measurement precision for individual sources.

Abstract

Abstract
Usually, Machine Learning systems are seen as something fully automatic. Recently, however, interactive systems in which human experts actively contribute towards the learning process have shown improved performance when compared to fully automated ones. This may be so in scenarios of Big Data, scenarios in which the input is a data stream, or when there is concept drift. In this paper, we present a system for supporting auditors in the task of financial fraud detection. The system is interactive in the sense that the auditors can provide feedback regarding the instances of the data they use, or even suggest new variables. This feedback is incorporated into newly trained Machine Learning models which improve over time.