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

Similarity Measures For Incomplete Database Instances

The problem of comparing database instances with incompleteness is prevalent in applications such as analyzing how a dataset has evolved over time (e.g., data versioning), evaluating data cleaning solutions (e.g., compare an instance produced by a data repair algorithm against a gold standard), or comparing solutions generated by data exchange systems (e.g., universal vs core solutions). In this work, we propose a framework for computing similarity of instances with labeled nulls, even of those without primary keys. As a side-effect, the similarity score computation returns a mapping between the instances’ tuples, which explains the score. We demonstrate that computing the similarity of two incomplete instances is NP-hard in the instance size in general. To be able to compare instances of realistic size, we present an approximate PTIME algorithm for instance comparison. Experimental results demonstrate that the approximate algorithm is up to three orders of magnitude faster than an exact algorithm for the computation of the similarity score, while the difference between approximate and exact scores is always smaller than 1%

Inclusion of Magnesium- and Strontium-Enriched Bioactive Glass into Electrospun PCL Scaffolds for Tissue Regeneration

Bioactive glass (BG) is a promising material known for its osteogenic, osteoinductive, antimicrobial, and angiogenic properties. For this reason, melt-quench-derived BG powders embedded into composite electrospun poly(epsilon-caprolactone) (PCL) mats represent an interesting option for the fabrication of bioactive scaffolds. However, incorporating BG into nano-/micro-fibers remains challenging. Our research focused on integrating two BG compositions into the mat structure: 45S5 and 45S5_MS (the former being a well-known, commercially available BG composition, and the latter a magnesium- and strontium-enriched composition based on 45S5). Both BG types were added at concentrations of 10 wt.% and 20 wt.%. A careful grinding process enabled effective dispersion of BG into a PCL solution, resulting in fibers ranging from 500 nm to 2 mu m in diameter. The mats' mechanical properties were not hindered by the inclusion of BG powder within the fibrous structure. Furthermore, our results indicate that BG powders were successfully incorporated into the scaffolds, not only preserving their properties but potentially enhancing their biological performance compared to unloaded PCL electrospun scaffolds. Our findings indicate proper cell differentiation and proliferation, supporting the potential of these devices for tissue regeneration applications

Signal enhancement and efficient DTW-based comparison for wearable gait recognition

The popularity of biometrics-based user identification has significantly increased over the last few years. User identification based on the face, fingerprints, and iris, usually achieves very high accuracy only in controlled setups and can be vulnerable to presentation attacks, spoofing, and forgeries. To overcome these issues, this work proposes a novel strategy based on a relatively less explored biometric trait, i.e., gait, collected by a smartphone accelerometer, which can be more robust to the attacks mentioned above. According to the wearable sensor-based gait recognition state-of-the-art, two main classes of approaches exist: 1) those based on machine and deep learning; 2) those exploiting hand-crafted features. While the former approaches can reach a higher accuracy, they suffer from problems like, e.g., performing poorly outside the training data, i.e., lack of generalizability. This paper proposes an algorithm based on hand-crafted features for gait recognition that can outperform the existing machine and deep learning approaches. It leverages a modified Majority Voting scheme applied to Fast Window Dynamic Time Warping, a modified version of the Dynamic Time Warping (DTW) algorithm with relaxed constraints and majority voting, to recognize gait patterns. We tested our approach named MV-FWDTW on the ZJU-gaitacc, one of the most extensive datasets for the number of subjects, but especially for the number of walks per subject and walk lengths. Results set a new state-of-the-art gait recognition rate of 98.82% in a cross-session experimental setup. We also confirm the quality of the proposed method using a subset of the OU-ISIR dataset, another large state-of-the-art benchmark with more subjects but much shorter walk signals

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