Abstract
Recently introduced Generalized Pre-trained Transformers (GPT) and conversional chatbots such as ChatGPT are causing deep society transformations. The incorporation of these Artificial Intelligence technologies can be beneficial in multiple science and development areas including Control Engineering. The evaluation of GPTs within Control Engineering Education and PID control is addressed in this work. Different types of interactions with GPTs are evaluated and the use of a personalized GPT for PID tuning explored. Copyright (C) 2024 The Authors. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract
Many natural signals exhibit a quasi-periodic behavior and are conveniently modeled as a combination of several harmonic sinusoids whose relative frequencies, magnitudes and phases vary with time. The waveform shape of those signals reflects important physical phenomena underlying their generation, which requires that those parameters be accurately estimated and modeled. In the literature, accurate phase estimation and modeling has received much less research effort than frequency estimation, or magnitude estimation. First, this paper addresses accurate DFT-based phase estimation of individual sinusoids in six scenarios involving two DFT-based filter banks and three different windows. It is shown that bias in phase estimation is less than 1E-3 radians when the SNR is equal to or larger than 2.5 dB. Taking as a reference the Cramér-Rao Lower Bound, it is shown that one particular window offers a performance of practical interest by approximating better the CRLB when signal conditions are favorable, and by minimizing the performance deviation when signal conditions are adverse. Second, this paper explains how a shift-invariant phase-related feature can be devised that characterizes harmonic phase structure, which motivates a signal processing paradigm that greatly simplifies parametric modeling, transformation and synthesis of harmonics signals, in addition to facilitating the understanding and reverse engineering of the phasegram. Theory and results are discussed in a reproducible perspective using dedicated experiments that are supported with code allowing not only to replicate figures and results in this paper, but also to expand research.

Abstract

Abstract
The latest technological advances drive the emergence of countless real-time data streams fed by users, sensors, and devices. These data sources can be mined with the help of predictive and classification techniques to support decision-making in fields like e-commerce, industry or health. In particular, stream-based classification is widely used to categorise incoming samples on the fly. However, the distribution of samples per class is often imbalanced, affecting the performance and fairness of machine learning models. To overcome this drawback, this paper proposes Bplug, a balancing plug-in for stream-based classification, to minimise the bias introduced by data imbalance. First, the plugin determines the class imbalance degree and then synthesises data statistically through non-parametric kernel density estimation. The experiments, performed with real data from Wikivoyage and Metro of Porto, show that Bplug maintains inter-feature correlation and improves classification accuracy. Moreover, it works both online and offline.

Abstract
Modern hardware accelerators, such as FPGAs, allow offloading large regions of C/C++ code in order to improve the execution time and/or the energy consumption of software applications. An outstanding challenge with this approach, however, is solving the Hardware/Software (Hw/Sw) partitioning problem. Given the increasing complexity of both the accelerators and the potential code regions, one needs to adopt a holistic approach when selecting an offloading region by exploring the interplay between communication costs, data usage patterns, and target-specific optimizations. To this end, we propose representing a C application as an extended task graph (ETG) with flexible granularity, which can be manipulated through the merging and splitting of tasks. This approach involves generating a task graph overlay on the program's Abstract Syntax Tree (AST) that maps tasks to functions and the flexible granularity operations onto inlining/outlining operations. This maintains the integrity and readability of the original source code, which is paramount for targeting different accelerators and enabling code optimizations, while allowing the offloading of code regions of arbitrary complexity based on the data patterns of their tasks. To evaluate the ETG representation and its compiler, we use the latter to generate ETGs for the programs in Rosetta and MachSuite benchmark suites, and extract several metrics regarding data communication, task-level parallelism, and dataflow patterns between pairs of tasks. These metrics provide important information that can be used by Hw/Sw partitioning methods.

Abstract
This paper presents GAMAI, an AI-powered exercise gamifier, enriching the Framework for Gamified Programming Education (FGPE) ecosystem. Leveraging OpenAI APIs, GAMAI enables the teachers to leverage the storytelling approach to describe the gamified scenario. GAMAI decorates the natural language text with sentences needed by OpenAI APIs to contextualize the prompt. Once the gamified scenario has been generated, GAMAI automatically produces the exercise files for the FGPE AuthorKit editor. We present preliminary results in AI-assessed gamified exercise generation, showing that most generated exercises are ready to be used with none or minimum human effort needed.