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.

Abstract
Many natural signals exhibit quasi-periodic behaviors and are conveniently modeled as combinations of several harmonic sinusoids whose relative frequencies, magnitudes, and phases vary with time. The waveform shapes of those signals reflect important physical phenomena underlying their generation, requiring those parameters to be accurately estimated and modeled. In the literature, accurate phase estimation and modeling have received significantly less attention than frequency or magnitude estimation. This paper first addresses accurate DFT-based phase estimation of individual sinusoids across six scenarios involving two DFT-based filter banks and three different windows. It has been shown that bias in phase estimation is less than 0.001 radians when the SNR is equal to or larger than 2.5 dB. Using the Cram & eacute;r-Rao lower bound as a reference, it has been demonstrated that one particular window offers performance of practical interest by better approximating the CRLB under favorable signal conditions and minimizing performance deviation under adverse conditions. This paper describes the development of a shift-invariant phase-related feature that characterizes the harmonic phase structure. This feature motivates a new signal processing paradigm that greatly simplifies the parametric modeling, transformation, and synthesis of harmonic signals. It also aids in understanding and reverse engineering the phasegram. The theory and results are discussed from a reproducible perspective, with dedicated experiments supported by code, allowing for the replication of figures and results presented in this paper and facilitating further research.

Abstract
As many automated algorithms find their way into the IT systems of the banking sector, having a way to validate and interpret the results from these algorithms can lead to a substantial reduction in the risks associated with automation. Usually, validating these pricing mechanisms requires human resources to manually analyze and validate large quantities of data. There is a lack of effective methods that analyze the time series and understand if what is currently happening is plausible based on previous data, without information about the variables used to calculate the price of the asset. This paper describes an implementation of a process that allows us to validate many data points automatically. We explore the K-Nearest Neighbors algorithm to find coincident patterns in financial time series, allowing us to detect anomalies, outliers, and data points that do not follow normal behavior. This system allows quicker detection of defective calculations that would otherwise result in the incorrect pricing of financial assets. Furthermore, our method does not require knowledge about the variables used to calculate the time series being analyzed. Our proposal uses pattern matching and can validate more than 58% of instances, substantially improving human risk analysts' efficiency. The proposal is completely transparent, allowing analysts to understand how the algorithm made its decision, increasing the trustworthiness of the method.