Extracting knowledge from digital social platforms is a new trend in software requirements engineering. The feedback generated by users in these environments is a very valuable source of needs, demands, dissatisfactions, and judgments about the software applications they use. The effectively processing of this volume of information for predicting the software product evolution is a great challenge, which can start from determining the most relevant contents, to the automatic elicitation of new requirements for that product. This paper proposes a method for generating software requirements from user feedback. This solution combines Deep Learning models for determining the relevant information, with an LLM (Large Language Model) to generate functional and non-functional requirements about a software and specific aspects from the feedback. This solution was evaluated using datasets of opinions from 4 different applications. The results were very promising, because they showed improvements over other reported solutions, and demonstrated that with a proper conception of information filtering and specification of interests through the prompt it is possible to generate good quality requirements using LLMs from an informal information source.
This paper presents a novel implementation of a Neural Network model in a low-power embedded system for burst detection in pipelines. It combines a quantized model with the LEA (Low-Energy Accelerator) and FRAM memory to perform signal processing and inference operations, optimizing the balance between accuracy and energy efficiency. The model was trained on a dataset of 187 signals (80 noise, 107 ruptures). Accuracy, precision, and recall metrics were used for model evaluation. After implementation in the microcontroller, a validation protocol was executed to assess the impact of quantization on the method’s accuracy.
Limited availability and class imbalance in tabular data make it difficult the development of effective models using machine learning methods. Synthetic data generation is a promising solution, but requires rigorous methodologies and extensive evaluation. This work proposes a methodology for generating and evaluating synthetic tabular data, especially from imbalanced datasets, illustrated with a case study in Intensive Care Units. The methodology comprises: (1) class balancing by oversampling, selecting the best technique with a quality metric; (2) synthetic data generation from the optimal balanced dataset, using SMOTE RSB* Adapted with Gaussian Noise, CTGAN and TVAE; and (3) a novel quality metric, TabDSFidelity, that integrates distributional similarity, correlation preservation, and predictive utility to guide the selection at each stage. Applied to ten datasets, the methodology demonstrated that the use of synthetic data selected by a quality metric significantly improves the performance of classification models compared to using only the original data. SMOTE RSB* Adapted with Gaussian Noise consistently generated the highest quality data according to TabDSFidelity in this study. It is concluded that the proposed methodology offers an effective framework for mitigating data sparsity and imbalance, facilitating the creation of more accurate and robust models.
In this paper a new embedded software package for efficiently computing the Continuous Wavelet Transform (CWT) in ARM Cortex cores, based on the original fast Continuous Transform (fCWT) algorithmic implementation is introduced. To execute the operations in ARM Cortex cores efficiently, the generic C++ implementation of the fCWT was adapted to a CMSIS-DSP based implementation in order to exploit the capabilities of the floating point unit (FPU) and the DSP instructions set with SIMD capabilities of ARM Cortex-M and Cortex-A cores. The package can be used for Continuous Wavelet Transform based feature extraction in edge computing signal processing applications that involve ARM based microcontroller families. A functional verification and a specific application oriented performance measurement approach in terms of real-time analysis ratio (RAR) for embedded processors is provided.
Accurate forecasting of the Consumer Price Index is crucial for economic stability, enabling policymakers to design effective monetary strategies and mitigate inflationary risks. Traditional models like ARIMA struggle to capture complex non-linear patterns and long-term dependencies in Consumer Price Index data, often leading to suboptimal predictive performance. To address these limitations, this study proposes a hybrid deep learning model combining a Deep Neural Network and a Transformer architecture. The Deep Neural Network extracts non-linear features, while the Transformer’s multi-head attention mechanism captures temporal dependencies, enhancing forecasting precision. The model incorporates advanced preprocessing techniques, including dynamic window normalization and outlier detection, ensuring robust data representation. Experimental results demonstrate superior performance, with a Mean Absolute Error (MAE) of 0.45, Root Mean Square Error (RMSE) of 0.62, and Mean Absolute Percentage Error (MAPE) of 1.8
