Abstract
Information and Communication Technologies are driving the improvement of industrial processes. According to the Industry 4.0 (I4.0) paradigm, digital systems provide real-time information to humans and machines, increasing flexibility and efficiency in production environments. Based on the I4.0 Design Principles concept, Virtual Assistants can play a vital role in processing production data and offer contextualized and real-time information to the workers in the production environment. This systematic review paper explored Virtual Assistant applications in the context of I4.0, discussing the Technical Assistance Design Principle and identifying the characteristics, services, and limitations regarding Virtual Assistant use in the production environments. The results showed that Virtual Assistants offer Physical and Virtual Assistance. Virtual Assistance provides real-time contextualized information mainly for support, while Physical Assistance is oriented toward task execution. Regarding services, the applications include integration with legacy systems and static information treatment. The limitations of the applications incorporate concerns about information security and adapting to noisy and unstable environments. It is possible to assume that the terminology of Virtual Assistants is not standardized and is mentioned as chatbots, robots, and others. Besides the worthy insights of this research, the small number of resulting papers did not allow for generalizations. Future research should focus on broadening the search scope to provide more-significant conclusions and research possibilities with new AI models and services, including the emergent Industry 5.0 concept.

Abstract
Globally, insect pests are the primary reason for reduced crop yield and quality. Although pesticides are commonly used to control and eliminate these pests, they can have adverse effects on the environment, human health, and natural resources. As an alternative, integrated pest management has been devised to enhance insect pest control, decrease the excessive use of pesticides, and enhance the output and quality of crops. With the improvements in artificial intelligence technologies, several applications have emerged in the agricultural context, including automatic detection, monitoring, and identification of insects. The purpose of this article is to outline the leading techniques for the automated detection of insects, highlighting the most successful approaches and methodologies while also drawing attention to the remaining challenges and gaps in this area. The aim is to furnish the reader with an overview of the major developments in this field. This study analysed 92 studies published between 2016 and 2022 on the automatic detection of insects in traps using deep learning techniques. The search was conducted on six electronic databases, and 36 articles met the inclusion criteria. The inclusion criteria were studies that applied deep learning techniques for insect classification, counting, and detection, written in English. The selection process involved analysing the title, keywords, and abstract of each study, resulting in the exclusion of 33 articles. The remaining 36 articles included 12 for the classification task and 24 for the detection task. Two main approaches-standard and adaptable-for insect detection were identified, with various architectures and detectors. The accuracy of the classification was found to be most influenced by dataset size, while detection was significantly affected by the number of classes and dataset size. The study also highlights two challenges and recommendations, namely, dataset characteristics (such as unbalanced classes and incomplete annotation) and methodologies (such as the limitations of algorithms for small objects and the lack of information about small insects). To overcome these challenges, further research is recommended to improve insect pest management practices. This research should focus on addressing the limitations and challenges identified in this article to ensure more effective insect pest management.

Abstract
In recent years, the amount of information generated, consumed and stored has grown at an astonishing rate, making it difficult for those seeking information to extract knowledge in good time. This has become even more important, as the average reader is not as willing to spare more time out of their already busy schedule as in the past, thus prioritizing news in a summarized format, which are faster to digest. On top of that, people tend to increasingly rely on strong visual components to help them understand the focal point of news articles in a less tiresome manner. This growing demand, focused on exploring information through visual aspects, urges the need for the emergence of alternative approaches concerned with text understanding and narrative exploration. This motivated us to propose Text2Storyline, a platform for generating and exploring enriched storylines from an input text, a URL or a user query. The latter is to be issued on the PortugueseWebArchive (Arquivo.pt), therefore giving users the chance to expand their knowledge and build up on information collected from web sources of the past. To fulfill this objective, we propose a system that makes use of the TimeMatters algorithm to filter out non-relevant dates and organize relevant content by means of different displays: `Annotated Text', `Entities', `Storyline', `Temporal Clustering' and `Word Cloud'. To extend the users' knowledge, we rely on entity linking to connect persons, events, locations and concepts found in the text to Wikipedia pages, a process also known as Wikification. Each of the entities is then illustrated by means of an image collected from the Arquivo.pt.

Abstract
BackgroundBreast cancer therapy improved significantly, allowing for different surgical approaches for the same disease stage, therefore offering patients different aesthetic outcomes with similar locoregional control. The purpose of the CINDERELLA trial is to evaluate an artificial-intelligence (AI) cloud-based platform (CINDERELLA platform) vs the standard approach for patient education prior to therapy. MethodsA prospective randomized international multicentre trial comparing two methods for patient education prior to therapy. After institutional ethics approval and a written informed consent, patients planned for locoregional treatment will be randomized to the intervention (CINDERELLA platform) or controls. The patients in the intervention arm will use the newly designed web-application (CINDERELLA platform, CINDERELLA APProach) to access the information related to surgery and/or radiotherapy. Using an AI system, the platform will provide the patient with a picture of her own aesthetic outcome resulting from the surgical procedure she chooses, and an objective evaluation of this aesthetic outcome (e.g., good/fair). The control group will have access to the standard approach. The primary objectives of the trial will be i) to examine the differences between the treatment arms with regards to patients' pre-treatment expectations and the final aesthetic outcomes and ii) in the experimental arm only, the agreement of the pre-treatment AI-evaluation (output) and patient's post-therapy self-evaluation. DiscussionThe project aims to develop an easy-to-use cost-effective AI-powered tool that improves shared decision-making processes. We assume that the CINDERELLA APProach will lead to higher satisfaction, better psychosocial status, and wellbeing of breast cancer patients, and reduce the need for additional surgeries to improve aesthetic outcome.

Abstract
Type systems and type inference systems can be used to help text and code generation models like GPT-3 produce more accurate and appropriate results. These systems provide information about the types of variables, functions, and other elements in a program or codebase, which can be used to guide the generation of new code or text. For example, a code generation model that is aware of the types of variables and functions being used in a program can generate code that is more likely to be syntactically correct and semantically meaningful. We argue for the specialization of language models such as GPT-3 for automatic program repair tasks, incorporating type information in the model's learning process. A trained language model is expected to perform better by understanding the nuances of type systems and using them for program repair, instead of just relying on the general structure of programs.

Abstract
The European Union (EU) established through the Common Agricultural Policy (CAP) an aid system and subsidies for farmers that cultivate vineyards. Eligible areas should be controlled and registered in Geographic Information Systems. The agencies paying this support must check that the parcels have an agricultural activity through an on-the-spot check or the analysis of aerial or satellite images. Abandonment situations lead to the cancellation of aid payments. In the Douro Demarcated Region of Portugal, inspections are conducted according to EU-defined methods. However, due to the vast size of the region, which spans approximately 250,000 hectares with vineyard cultures occupying 43,843 hectares, the analysis time and specialized human resources required for these inspections are significant. In this study, we curated a new dataset for training convolutional neural networks (CNNs) and fine-tuned pre-trained VGG models to classify vineyards as abandoned or non-abandoned. The baseline model achieved an accuracy of 95.1% on the test dataset, while the top-performing model achieved an impressive overall accuracy and F1-score of 99% for both classes.