arXiv subjects

Rodolfo Azevedo

Publications and source records attributed to Rodolfo Azevedo.

5 recordsLinked to original sources

arXiv2025

Global Overview of Computational Thinking and Digital Tools for Teaching

Computational Thinking (CT) has emerged as a critical component in modern education, essential to equip students with the skills necessary to thrive in a technology-driven world. This survey provides a comprehensive analysis of the presence and integration of CT in school curricula across various countries. In addition, this study categorizes digital tools into groups such as visual programming, textual programming, electronic games, modeling, and simulation, assessing their use in different educational settings. Furthermore, it examines how these tools are employed in various contexts, including the areas of knowledge and age groups they target, and the specific skills they help develop. The research also identifies key CT competencies that have been improved through these tools, including Cognitive and Analytical Competencies (CAC), Technical and Computational Competencies (TCC) and Social and Emotional Competencies (SEC). Furthermore, the study highlights recurring challenges in the implementation of digital tools for CT development, such as inadequate infrastructure, difficulties in the usability of the tool, teacher training, adapting pedagogical practices, and measuring student CT skills. Finally, it proposes areas for future research to address these challenges and advance CT education.

cs.CY↗

arXiv2025

Who Introduces and Who Fixes? Analyzing Code Quality in Collaborative Student's Projects

This paper investigates code quality education by analyzing how errors are introduced and corrected in group projects within an embedded systems course. We identify who introduces errors, who fixes them, and when these actions occur. Students learn code quality rules for C and embedded systems. We address three questions: RQ1: What is the impact of group formation on code quality? RQ2: How do students interact to fix code issues? RQ3: When are issues introduced and resolved? We analyzed data from eight individual labs and two group projects involving 34 students. The course provides continuous, automated feedback on code quality. Findings show that the most active contributors often introduce the most issues. Many issues are fixed late in the project. Individual labs tend to have fewer issues due to their structured nature. Most problems are fixed by the original author, while cross-student fixes take longer, especially in shared code. Critical issues are fixed quickly, but non-critical ones may be ignored, showing a focus on functionality over quality.

cs.SE↗

arXiv2022

The Uhlenbeck-Ford model: Exact virial coefficients and application as a reference system in fluid-phase free-energy calculations

The Uhlenbeck-Ford (UF) model was originally proposed for the theoretical study of imperfect gases, given that all its virial coefficients can be evaluated exactly, in principle. Here, in addition to computing the previously unknown coefficients B11 through B13, we assess its applicability as a reference system in fluid-phase free-energy calculations using molecular simulation techniques. Our results demonstrate that, although the UF model itself is too soft, appropriately scaled Uhlenbeck- Ford (sUF) models provide robust reference systems that allow accurate fluid-phase free-energy calculations without the need for an intermediate reference model. Indeed, in addition to the accuracy with which their free energies are known and their convenient scaling properties, the fluid is the only thermodynamically stable phase for a wide range of sUF models. This set of favorable properties may potentially put the sUF fluid-phase reference systems on par with the standard role that harmonic and Einstein solids play as reference systems for solid-phase free-energy calculations.

cond-mat.mtrl-sci↗

arXiv2020

Optically Connected Memory for Disaggregated Data Centers

Recent advances in integrated photonics enable the implementation of reconfigurable, high-bandwidth, and low energy-per-bit interconnects in next-generation data centers. We propose and evaluate an Optically Connected Memory (OCM) architecture that disaggregates the main memory from the computation nodes in data centers. OCM is based on micro-ring resonators (MRRs), and it does not require any modification to the DRAM memory modules. We calculate energy consumption from real photonic devices and integrate them into a system simulator to evaluate performance. Our results show that (1) OCM is capable of interconnecting four DDR4 memory channels to a computing node using two fibers with 1.07 pJ energy-per-bit consumption and (2) OCM performs up to 5.5x faster than a disaggregated memory with 40G PCIe NIC connectors to computing nodes.

cs.AR↗

arXiv2019

Dataplant: Enhancing System Security with Low-Cost In-DRAM Value Generation Primitives

DRAM manufacturers have been prioritizing memory capacity, yield, and bandwidth for years, while trying to keep the design complexity as simple as possible. DRAM chips do not carry out any computation or other important functions, such as security. Processors implement most of the existing security mechanisms that protect the system against security threats, because 1) executing security mechanisms usually require non-trivial computational capabilities (e.g., encryption), and 2) commodity DRAM chips are not designed to perform computations or tasks other than data storage. In this work, we advocate for DRAM as a key component for providing security mechanisms to the system. To this end, we propose Dataplant, a new class of low-cost, high-performance, and reliable security primitives that can be integrated in commodity DRAM chips with minimal changes. The main idea of Dataplant is to slightly modify the internal DRAM timing signals to expose the inherent process variation found in all DRAM chips for generating unpredictable but reproducible values (e.g., keys) within DRAM. We use Dataplant to build two new security mechanisms. First, a new Dataplant-based physical unclonable function (PUF) with non-destructive read-out, low evaluation latency, robust responses, resiliency to temperature changes, and data-independent responses. Second, a new cold boot attack prevention mechanism that automatically destroys all data within DRAM on every power cycle with zero run-time energy and latency overheads. Using a combination of detailed simulations and experiments with 136 real commodity DRAM chips, we show that our Dataplant-based PUF has 1.8x higher throughput than the best state-of-the-art DRAM PUFs. We also demonstrate that our Dataplant-based cold boot attack protection mechanism is 19.5x faster and consumes 2.54x less energy when compared to existing mechanisms.

cs.CR↗