Diogene-CT: tools and methodologies for teaching and learning coding

Computational thinking is the capacity of undertaking a problem-solving process in various disciplines (including STEM, i.e. science, technology, engineering and mathematics) using distinctive techniques that are typical of computer science. It is nowadays considered a fundamental skill for students and citizens, that has the potential to affect future generations. At the roots of computational-thinking abilities stands the knowledge of computer programming, i.e. coding. With the goal of fostering computational thinking in young students, we address the challenging and open problem of using methods, tools and techniques to support teaching and learning of computer-programming skills in school curricula of the secondary grade and university courses. This problem is made complex by several factors. In fact, coding requires abstraction capabilities and complex cognitive skills such as procedural and conditional reasoning, planning, and analogical reasoning. In this paper, we introduce a new paradigm called ACME (“Code Animation by Evolved Metaphors”) that stands at the foundation of the Diogene-CT code visualization environment and methodology. We develop consistent visual metaphors for both procedural and object-oriented programming. Based on the metaphors, we introduce a playground architecture to support teaching and learning of the principles of coding. To the best of our knowledge, this is the first scalable code visualization tool using consistent metaphors in the field of the Computing Education Research (CER). It might be considered as a new kind of tools named as code visualization environments

Spice-up your coding lessons with the ACME approach

It is nowadays considered a fundamental skill for students and citizens the capacity of undertaking a problem-solving process in various disciplines (including STEM, i.e. science, technology, engineering and mathematics) using distinctive techniques that are typical of computer science. These abilities are usually called Computational Thinking and at the roots of them stands the knowledge of coding. With the goal of encouraging Computational Thinking in young students, we discuss tools and techniques to support the teaching and the learning of coding in school curricula. It is well known that this problem is complex due to the fact that it requires abstraction capabilities and complex cognitive skills such as procedural and conditional reasoning, planning, and analogical reasoning. In this paper, we present ACME (“Code Animation by Evolved Metaphors”) that stands at the foundation of the Diogene-CT code visualization environment and methodology. We discuss visual metaphors for both procedural and object-oriented programming. Based on them, we introduce a playground architecture to support teaching and learning of the principles of coding. To the best of our knowledge, this is the first scalable code visualization tool using consistent metaphors in the field of Computing Education Research (CER)

Cleaning data with Llunatic

Data cleaning (or data repairing) is considered a crucial problem in many database-related tasks. It consists in making a database consistent with respect to a given set of constraints. In recent years, repairing methods have been proposed for several classes of constraints. These methods, however, tend to hard-code the strategy to repair conflicting values and are specialized toward specific classes of constraints. In this paper, we develop a general chase-based repairing framework, referred to as Llunatic, in which repairs can be obtained for a large class of constraints and by using different strategies to select preferred values. The framework is based on an elegant formalization in terms of labeled instances and partially ordered preference labels. In this context, we revisit concepts such as upgrades, repairs and the chase. In Llunatic, various repairing strategies can be slotted in, without the need for changing the underlying implementation. Furthermore, Llunatic is the first data repairing system which is DBMS-based. We report experimental results that confirm its good scalability and show that various instantiations of the framework result in repairs of good quality

Photometric stereo with only two images: A theoretical study and numerical resolution

This work tackles the problem of two-image photometric stereo. This problem constitutes the intermediate case between conventional photometric stereo with at least three images, which is well-posed, and shape-from-shading, which is ill-posed. We first provide a theoretical study of ambiguities arising in this intermediate case. Based on this study, we show that when the albedo is known, disambiguation can be formulated as a binary labeling problem, using integrability and a nonstationary Ising model. The resulting optimization problem is solved efficiently by resorting to the graph cut algorithm. These theoretical and numerical contributions are eventually validated in an application to three-image photometric stereo with shadows.Roberto Mecca was a Marie Curie Fellow of the Instituto Nazionale di Alta Matematic

A Novel Transformer-Based IMU Self-Calibration Approach through On-Board RGB Camera for UAV Flight Stabilization

During flight, unmanned aerial vehicles (UAVs) need several sensors to follow a predefined path and reach a specific destination. To this aim, they generally exploit an inertial measurement unit (IMU) for pose estimation. Usually, in the UAV context, an IMU entails a three-axis accelerometer and a three-axis gyroscope. However, as happens for many physical devices, they can present some misalignment between the real value and the registered one. These systematic or occasional errors can derive from different sources and could be related to the sensor itself or to external noise due to the place where it is located. Hardware calibration requires special equipment, which is not always available. In any case, even if possible, it can be used to solve the physical problem and sometimes requires removing the sensor from its location, which is not always feasible. At the same time, solving the problem of external noise usually requires software procedures. Moreover, as reported in the literature, even two IMUs from the same brand and the same production chain could produce different measurements under identical conditions. This paper proposes a soft calibration procedure to reduce the misalignment created by systematic errors and noise based on the grayscale or RGB camera built-in on the drone. Based on the transformer neural network architecture trained in a supervised learning fashion on pairs of short videos shot by the UAV’s camera and the correspondent UAV measurements, the strategy does not require any special equipment. It is easily reproducible and could be used to increase the trajectory accuracy of the UAV during the flight

A Novel GAN-Based Anomaly Detection and Localization Method for Aerial Video Surveillance at Low Altitude

The last two decades have seen an incessant growth in the use of Unmanned Aerial Vehicles (UAVs) equipped with HD cameras for developing aerial vision-based systems to support civilian and military tasks, including land monitoring, change detection, and object classification. To perform most of these tasks, the artificial intelligence algorithms usually need to know, a priori, what to look for, identify. or recognize. Actually, in most operational scenarios, such as war zones or post-disaster situations, areas and objects of interest are not decidable a priori since their shape and visual features may have been altered by events or even intentionally disguised (e.g., improvised explosive devices (IEDs)). For these reasons, in recent years, more and more research groups are investigating the design of original anomaly detection methods, which, in short, are focused on detecting samples that differ from the others in terms of visual appearance and occurrences with respect to a given environment. In this paper, we present a novel two-branch Generative Adversarial Network (GAN)-based method for low-altitude RGB aerial video surveillance to detect and localize anomalies. We have chosen to focus on the low-altitude sequences as we are interested in complex operational scenarios where even a small object or device can represent a reason for danger or attention. The proposed model was tested on the UAV Mosaicking and Change Detection (UMCD) dataset, a one-of-a-kind collection of challenging videos whose sequences were acquired between 6 and 15 m above sea level on three types of ground (i.e., urban, dirt, and countryside). Results demonstrated the effectiveness of the model in terms of Area Under the Receiving Operating Curve (AUROC) and Structural Similarity Index (SSIM), achieving an average of 97.2% and 95.7%, respectively, thus suggesting that the system can be deployed in real-world applications

Assessing the effects of Ang-(1-7) therapy following transient middle cerebral artery occlusion

The counter-regulatory axis, Angiotensin Converting Enzyme 2, Angiotensin-(1-7), Mas receptor (ACE2/Ang-1-7/MasR), of the renin angiotensin system (RAS) is a potential therapeutic target in stroke, with Ang-(1-7) reported to have neuroprotective effects in pre-clinical stroke models. Here, an extensive investigation of the functional and mechanistic effects of Ang-(1-7) was performed in a rodent model of stroke. Using longitudinal magnetic resonance imaging (MRI) it was observed that central administration of Ang-(1-7) following transient middle cerebral artery occlusion (MCAO) increased the amount of tissue salvage compared to reperfusion alone. This protective effect was not due to early changes in blood brain barrier (BBB) permeability, microglia activation or inflammatory gene expression. However, increases in NADPH oxidase 1 (Nox1) mRNA expression were observed in the treatment group compared to control. In order to determine whether Ang-(1-7) has direct cerebrovascular effects, laser speckle contrast imaging (LSCI) was performed to measure dynamic changes in cortical perfusion following reperfusion. Delivery of Ang-(1-7) did not have any effect on cortical perfusion following reperfusion however; it showed an indication to prevent the ‘steal phenomenon’ within the contralateral hemisphere. The comprehensive series of studies have demonstrated a moderate protective effect of Ang-(1-7) when given alongside reperfusion to increase tissue salvage

Developing an algorithm for pulse oximetry derived respiratory rate (RRoxi): a healthy volunteer study

Objective The presence of respiratory information within the pulse oximeter signal (PPG) is a well-documented phenomenon. However, extracting this information for the purpose of continuously monitoring respiratory rate requires: (1) the recognition of the multi-faceted manifestations of respiratory modulation components within the PPG and the complex interactions among them; (2) the implementation of appropriate advanced signal processing techniques to take full advantage of this information; and (3) the post-processing infrastructure to deliver a clinically useful reported respiratory rate to the end user. A holistic algorithmic approach to the problem is therefore required. We have developed the RROXI algorithm based on this principle and its performance on healthy subject trial data is described herein