Abstract
Human-Computer Interaction (HCI) research has illuminated how technology can influence users’ awareness of their environmental impact and the potential for mitigating these impacts. From hot water saving to food waste reduction, researchers have systematically and widely tried to find pathways to speed up achieving sustainable development goals through persuasive technology interventions. However, motivating users to adopt sustainable behaviors through interactive technologies presents significant psychological, cultural, and technical challenges in creating engaging and long-lasting experiences. Aligned with this perspective, there is a dearth of research and design solutions addressing the use of persuasive technology to promote sustainable recycling behavior. Guided by a participatory design approach, this investigation focuses on the design opportunities for leveraging persuasive and human-centered Internet of Things (IoT) applications to enhance user engagement in recycling activities. The assumption is that one pathway to achieve this goal is to adopt persuasive strategies that may be incorporated into the design of sustainable applications. The insights gained from this process can then be applied to various sustainable HCI scenarios and therefore contribute to HCI’s limited understanding in this area by providing a series of design-oriented research recommendations for informing the development of persuasive and socially engaged recycling platforms. In particular, we advocate for the inclusion of educational content, real-time interactive feedback, and intuitive interfaces to actively engage users in recycling activities. Moreover, recognizing the cultural context in which the technology is socially situated becomes imperative for the effective implementation of smart devices to foster sustainable recycling practices. To this end, we present a case study that seeks to involve children and adolescents in pro-recycling activities within the school environment.

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
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
Breast cancer is the most common type of cancer in women worldwide. Because of high survival rates, there has been an increased interest in patient Quality of Life after treatment. Aesthetic results play an important role in this aspect, as these treatments can leave a mark on a patient's self-image. Despite that, there are no standard ways of assessing aesthetic outcomes. Commonly used software such as BCCT.core or BAT require the manual annotation of keypoints, which makes them time-consuming for clinical use and can lead to result variability depending on the user. Recently, there have been attempts to leverage both traditional and Deep Learning algorithms to detect keypoints automatically. In this paper, we compare several methods for the detection of Breast Endpoints across two datasets. Furthermore, we present an extended evaluation of using these models as input for full contour prediction and aesthetic evaluation using the BCCT.core software. Overall, the YOLOv9 model, fine-tuned for this task, presents the best results considering both accuracy and usability, making this architecture the best choice for this application. The main contribution of this paper is the development of a pipeline for full breast contour prediction, which reduces clinician workload and user variability for automatic aesthetic assessment.