Abstract
Acquiring high-quality annotated data remains one of the most significant challenges in Natural Language Processing (NLP), especially for supervised learning approaches. In scenarios where pre-existing labeled data is unavailable, common solutions like crowdsourcing and zero-shot approaches often fall short, suffering from limitations such as the need for large datasets and a lack of guarantees regarding annotation quality. Traditionally, data for human annotation has been selected randomly, a practice that is not only costly and inefficient but also prone to bias, particularly in imbalanced datasets where minority classes are underrepresented. To address these challenges, this work introduces an automatic and informed data selection architecture designed to minimize the volume of required annotations while maximizing the diversity and representativeness of the selected data. Among the evaluated methods, Reverse Semantic Search (RSS) demonstrated superior performance, consistently outperforming random sampling in imbalanced scenarios and enhancing the effectiveness of trained classifiers. Furthermore, we compared RSS with other clustering-based approaches, providing insights into their respective strengths and weaknesses.

Abstract
Riding a motorcycle involves risks that can be minimized through advanced sensing and response systems to assist the rider. The use of camera-collected images to monitor road conditions can aid in the development of tools designed to enhance rider safety and prevent accidents. This paper proposes a method for developing deep learning models designed to operate efficiently on embedded systems like the Raspberry Pi, facilitating real-time decisions that consider the road condition. Our research tests and compares several state-of-the-art convolutional neural network architectures, including EfficientNet and Inception, to determine which offers the best balance between inference time and accuracy. Specifically, we measured top-1 accuracy and inference time on a Raspberry Pi, identifying EfficientNetV2 as the most suitable model due to its optimal trade-off between performance and computational demand. The model's top-1 accuracy significantly outperformed other models while maintaining competitive inference speeds, making it ideal for real-time applications in traffic-dense urban settings.

Abstract
With the global population on the rise and a declining agricultural labor force, the realm of robotics research in agriculture, such as robotic manipulators, has assumed heightened significance. This article undertakes a comprehensive exploration of the latest advancements in controllers tailored for robotic manipulators. The investigation encompasses an examination of six distinct controller paradigms, complemented by the presentation of three exemplars for each category. These paradigms encompass: (i) adaptive control, (ii) sliding mode control, (iii) model predictive control, (iv) robust control, (v) fuzzy logic control and (vi) neural network control. The article further introduces and presents comparative tables for each controller category. These controllers excel in tracking trajectories and efficiently reaching reference points with rapid convergence. The key point of divergence among these controllers resides in their inherent complexity.

Abstract
Breast cancer treatments often affect patients' body image, making aesthetic outcome predictions vital. This study introduces a Deep Learning (DL) multimodal retrieval pipeline using a dataset of 2,193 instances combining clinical attributes and RGB images of patients' upper torsos. We evaluate four retrieval techniques: Weighted Euclidean Distance (WED) with various configurations and shallow Artificial Neural Network (ANN) for tabular data, pre-trained and fine-tuned Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs), and a multimodal approach combining both data types. The dataset, categorised into Excellent/Good and Fair/Poor outcomes, is organised into over 20K triplets for training and testing. Results show fine-tuned multimodal ViTs notably enhance performance, achieving up to 73.85% accuracy and 80.62% Adjusted Discounted Cumulative Gain (ADCG). This framework not only aids in managing patient expectations by retrieving the most relevant post-surgical images but also promises broad applications in medical image analysis and retrieval. The main contributions of this paper are the development of a multimodal retrieval system for breast cancer patients based on post-surgery aesthetic outcome and the evaluation of different models on a new dataset annotated by clinicians for image retrieval.

Abstract
Information Systems (IS) projects are critical for organizational development, both in the private and public sectors. The relevance and complexity inherent in this type of project require management to be fully aware of the factors that influence success. This study contributes to the literature on public-sector IS project management by providing a comprehensive set of Success Factors (SFs) for different levels of the administration. The research method comprised a literature review, six case studies of central government, local government, and other types of administration, and a questionnaire-based survey of public sector IS experts. Forty-four SFs were identified, described, and organized in nine categories: organization and environment; strategy; project; scope; project manager and project team; stakeholders; vendors; clients and users; and monitoring and control. Our results add a new perspective to the theoretical body of knowledge on the SFs for IS projects in the public sector.

Abstract