We report on the use of a GenAI-based chatbot as a replacement for the traditional project specification document in a second-year object-oriented programming course. Instead of receiving a written specification, students interacted with a fictional non-technical client, Tio Bob, who answered questions about the expected behavior of a board game they had to implement. The chatbot used an LLM-based backend with safeguard features (e.g., code blocking), and the platform logged all exchanges for instructor analysis. We present a preliminary analysis of these logs, which included 13,816 interactions across a cohort of 97 students (median 106 per student) with an average prompt length of 14.1 words.

Wound dressings and their proper management are crucial to the wound's recovery. This process can be time-consuming and requires special knowledge to be effective. In order to improve the monitorization and decision-making processes, this work proposes a framework based on state-of-the-art Deep Learning models for automating the acquisition and analysis of wound dressings. Its development was supported by a novel dataset of dressing images annotated by experts regarding dressing state and regions of interest. The two-step acquisition pipeline resorts to a RetinaNet model to detect the dressing region, along with a reference marker, further used by the image validation module to ensure that the images fulfil the clinical adequacy requirements, such as the presence of a minimum periwound area. On top of its robust detection performance, its mobile deployment demonstrated its ability to support and standardise the image acquisition task. The characterisation module analyses the dressing areas provided by the detection model, using a MobileNetV3Small model to classify if the dressing is usable or in need of change and achieving an F1 score of 0.778. Thus, this work provides a promising system to streamline the dressing monitoring process, constituting an important advancement in this field with the potential to empower caregivers and healthcare professionals.

Wound monitoring is a time-consuming and error-prone activity performed daily by healthcare professionals. Capturing wound images is crucial in the current clinical practice, though image inadequacy can undermine further assessments. To provide sufficient information for wound analysis, the images should also contain a minimal periwound area. This work proposes an automatic wound image acquisition methodology that exploits deep learning models to guarantee compliance with the mentioned adequacy requirements, using a marker as a metric reference. A RetinaNet model detects the wound and marker regions, further analysed by a post-processing module that validates if both structures are present and verifies that a periwound radius of 4 centimetres is included. This pipeline was integrated into a mobile application that processes the camera frames and automatically acquires the image once the adequacy requirements are met. The detection model achieved [email protected] values of 0.39 and 0.95 for wound and marker detection, exhibiting a robust detection performance for varying acquisition conditions. Mobile tests demonstrated that the application is responsive, requiring 1.4 seconds on average to acquire an image. The robustness of this solution for real-time smartphone-based usage evidences its capability to standardise the acquisition of adequate wound images, providing a powerful tool for healthcare professionals.

Recent studies show that AI-driven code generation tools, such as Large Language Models, are able to solve most of the problems usually presented in introductory programming classes. However, it is still unknown how they cope with Object Oriented Programming assignments, where the students are asked to design and implement several interrelated classes (either by composition or inheritance) that follow a set of best-practices. Since the majority of the exercises in these tools' training dataset are written in English, it is also unclear how well they function with exercises published in other languages.

The introduction of gamification principles into the classroom may enhance and extend the student engagement in learning activities, and therefore, contribute to a more efficient knowledge acquisition. In this paper, we report a case study whose methodology is based on awarding merit badges as a result of the successful completion of learning activities focused on either soft and hard skills. This initiative was implemented in two universities, involving 221 participants, aimed at assessing how the proposed model may contribute to students’ motivation, appealing for its participation in the classroom, and measuring their stimulation for continuous study. Early stage results are promising and suggest not only a higher acceptance of the system and its effectiveness, but also, the suitability of gamification and reward systems on education. Further studies should be implemented in order to determine the effect of the reward system on students’ grades.

In recent times, infrastructure-free indoor positioning has been an important topic of research. Many of the proposed systems are based on pedestrian dead reckoning, thus relying on estimating the heading of the pedestrian. While many studies successfully address the problem of estimating the heading of the device, current approaches have the limitation of requiring the device to be aligned with the pedestrian. To address this problem, we propose an algorithm for estimating the misalignment between the pedestrian and the device by evaluating the acceleration data fit a simplified gait model in each direction. Contrary to similar algorithms, the proposal in this paper does not require a previously trained model nor the detection of steps, and can be implemented using only acceleration data. Furthermore, our experimental results show a significant improvement over the current state of the art.

Machine learning is an integral part of computational biology, and has already shown its use in various applications, such as prognostic tests. In the last few years in the non-biological machine learning community, ensembling techniques have shown their power in data mining competitions such as the Netflix challenge; however, such methods have not found wide use in computational biology. In this work, we endeavor to show how ensembling techniques can be applied to practical problems, including problems in the field of bioinformatics, and how they often outperform other machine learning techniques in both predictive power and robustness. Furthermore, we develop a methodology of ensembling, Multi-Swarm Ensemble (MSWE) by using multiple particle swarm optimizations and demonstrate its ability to further enhance the performance of ensembles.

Context-aware systems take into account the user's current context (such as location, time and activity) to enrich the user interaction with the application. However, these systems may produce huge amounts of information that must be efficiently propagated to a group of people or even large communities while still protecting the privacy of the participants.

Multi-user chat (MUC) applications raise serious challenges to developers concerning scalability and efficient use of network bandwidth, due to a large number of users exchanging lots of messages in real-time. We propose a new approach to MUC message propagation based on an adaptable consistency model bounded by three metrics: Filter, Time and Volume. In this model, the server propagates some messages as soon as possible while others are postponed until certain conditions are met, according to each client consistency requirements. These requirements can change during the session lifetime, constantly adapting to each client's current context.

We present a network framework for analyzing multi-level regulation in higher eukaryotes based on systematic integration of various high-throughput datasets. The network, namely the integrated regulatory network, consists of three major types of regulation: TF→gene, TF→miRNA and miRNA→gene. We identified the target genes and target miRNAs for a set of TFs based on the ChIP-Seq binding profiles, the predicted targets of miRNAs using annotated 3'UTR sequences and conservation information. Making use of the system-wide RNA-Seq profiles, we classified transcription factors into positive and negative regulators and assigned a sign for each regulatory interaction. Other types of edges such as protein-protein interactions and potential intra-regulations between miRNAs based on the embedding of miRNAs in their host genes were further incorporated. We examined the topological structures of the network, including its hierarchical organization and motif enrichment. We found that transcription factors downstream of the hierarchy distinguish themselves by expressing more uniformly at various tissues, have more interacting partners, and are more likely to be essential. We found an over-representation of notable network motifs, including a FFL in which a miRNA cost-effectively shuts down a transcription factor and its target. We used data of C. elegans from the modENCODE project as a primary model to illustrate our framework, but further verified the results using other two data sets. As more and more genome-wide ChIP-Seq and RNA-Seq data becomes available in the near future, our methods of data integration have various potential applications.

MOTIVATION: One of the major problems in shotgun proteomics is the low peptide coverage when analyzing complex protein samples. Identifying more peptides, e.g. non-tryptic peptides, may increase the peptide coverage and improve protein identification and/or quantification that are based on the peptide identification results. Searching for all potential non-tryptic peptides is, however, time consuming for shotgun proteomics data from complex samples, and poses a challenge for a routine data analysis. RESULTS: We hypothesize that non-tryptic peptides are mainly created from the truncation of regular tryptic peptides before separation. We introduce the notion of truncatability of a tryptic peptide, i.e. the probability of the peptide to be identified in its truncated form, and build a predictor to estimate a peptide's truncatability from its sequence. We show that our predictions achieve useful accuracy, with the area under the ROC curve from 76% to 87%, and can be used to filter the sequence database for identifying truncated peptides. After filtering, only a limited number of tryptic peptides with the highest truncatability are retained for non-tryptic peptide searching. By applying this method to identification of semi-tryptic peptides, we show that a significant number of such peptides can be identified within a searching time comparable to that of tryptic peptide identification.