Abstract
The co-creation of games is a research area that has shown very promising results in identifying technological requirements. It is an approach where the researcher usually adopts the role of a participant observer, guiding the dynamics of co-creation acts. This situation limits the opportunities for replicability of co-creation methods by independent facilitators, which could elucidate the quality and improvement opportunities of these methods, contributing to their more widespread application. In this paper, we present a methodology that aims to overcome this limitation, allowing the replication of co-creation workshops by different independent facilitators. This methodology was conceived in the context of collecting relevant information for the design of an educational digital platform that intends to use gamified resources for adult education in digital health data literacy. Specifically, co-creation workshops were used to gain an overview of the difficulties of different age groups in this area and their perspective on which games would best address these difficulties. The workshops were conducted in five countries with planning oriented so that each country could have a different facilitator, not requiring the presence of the researcher who designed them. The challenge of this planning was to maintain the approach of the facilitators identical in all countries, as best one could. We present here the method adopted through its planning and materials designed for information collection, which included brainstorming using card sorting and game ideation with the use of templates. The analysis of replicability by independent facilitators was done by scrutinizing the produced elements, which allowed us to observe the aspects of coherence and divergence among the various facilitators. Thus, we conclude that this approach is a good starting point to overcome current limitations and identify possible lines of improvement.

Abstract
Grapevines (Vitis vinifera L.) are one of the most economically relevant crops worldwide, yet they are highly vulnerable to various diseases, causing substantial economic losses for winegrowers. This systematic review evaluates the application of remote sensing and proximal tools for vineyard disease detection, addressing current capabilities, gaps, and future directions in sensor-based field monitoring of grapevine diseases. The review covers 104 studies published between 2008 and October 2024, identified through searches in Scopus and Web of Science, conducted on 25 January 2024, and updated on 10 October 2024. The included studies focused exclusively on the sensor-based detection of grapevine diseases, while excluded studies were not related to grapevine diseases, did not use remote or proximal sensing, or were not conducted in field conditions. The most studied diseases include downy mildew, powdery mildew, Flavescence dor & eacute;e, esca complex, rots, and viral diseases. The main sensors identified for disease detection are RGB, multispectral, hyperspectral sensors, and field spectroscopy. A trend identified in recent published research is the integration of artificial intelligence techniques, such as machine learning and deep learning, to improve disease detection accuracy. The results demonstrate progress in sensor-based disease monitoring, with most studies concentrating on specific diseases, sensor platforms, or methodological improvements. Future research should focus on standardizing methodologies, integrating multi-sensor data, and validating approaches across diverse vineyard contexts to improve commercial applicability and sustainability, addressing both economic and environmental challenges.

Abstract
Story components, namely, events, time, participants, and their relations are present in narrative texts from different domains such as journalism, medicine, finance, and law. The automatic extraction of narrative elements encompasses several NLP tasks such as Named Entity Recognition, Semantic Role Labeling, Event Extraction, and Temporal Inference. The text2story Python, an easy-to-use modular library, supports the narrative extraction and visualization pipeline. The package contains an array of narrative extraction tools that can be used separately or in sequence. With this toolkit, end users can process free text in English or Portuguese and obtain formal representations, like standard annotation files or a formal logical representation. The toolkit also enables narrative visualization as Message Sequence Charts (MSC), Knowledge Graphs, and Bubble Diagrams, making it useful to visualize and transform human-annotated narratives. The package combines the use of off-the-shelf and custom tools and is easily patched (replacing existing components) and extended (e.g. with new visualizations). It includes an experimental module for narrative element effectiveness assessment and being is therefore also a valuable asset for researchers developing solutions for narrative extraction. To evaluate the baseline components, we present some results of the main annotators embedded in our package for datasets in English and Portuguese. We also compare the results with the extraction of narrative elements by GPT-3, a robust LLM model.

Abstract

Abstract
E-commerce has become an essential aspect of modern life, providing consumers globally with convenience and accessibility. However, the high volume of short and noisy product descriptions in text streams of massive e-commerce platforms translates into an increased number of clusters, presenting challenges for standard model-based stream clustering algorithms. Standard LDA-based methods often lead to clusters dominated by single elements, effectively failing to manage datasets with varied cluster sizes. Our proposed Community-Based Topic Modeling with Contextual Outlier Handling (CB-TMCOH) algorithm introduces an approach to outlier detection in text data using transformer models for similarity calculations and graph-based clustering. This method efficiently separates outliers and improves clustering in large text datasets, demonstrating its utility not only in e-commerce applications but also proving effective for news and tweets datasets.

Abstract
The proposed framework (Human-Centered Trustworthy Framework) provides a novel human-computer interaction approach to incorporate positive and meaningful trustful user experiences in the system design process. It helps to illustrate potential users' trust concerns in artificial intelligence and guides nonexperts to avoid designing vulnerable interactions that lead to breaches of trust.