Abstract

Abstract
Advances in artificial intelligence trigger transformations that make more and more companies enter Industry 4.0 and 5.0 eras. In many cases, these transformations are gradual and performed in a bottom-up manner. This means that in the first step, the industrial hardware is upgraded to collect as much data as possible without actual planning of the utilization of the information. Furthermore, the data storage and processing infrastructure is prepared to keep large volumes of historical data accessible for further analysis. Only in the last step are methods for processing the data developed to improve or gain more insight into the industrial and business processes. Such a pipeline makes many companies face a problem with huge amounts of data, an incomplete understanding of how the existing knowledge is represented in the data, under which conditions the knowledge no longer holds, or what new phenomena are hidden inside the data. We argue that this gap needs to be addressed by the next generation of XAI methods which should be expert-oriented and focused on knowledge generation tasks rather than model debugging. The paper is based on the findings of the EU CHIST-ERA project on Explainable Predictive Maintenance (XPM).

Abstract
Liquid-based cytology (LBC) plays a crucial role in the effective early detection of cervical cancer, contributing to substantially decreasing mortality rates. However, the visual examination of microscopic slides is a challenging, time-consuming, and ambiguous task. Shortages of specialized staff and equipment are increasing the interest in developing artificial intelligence (AI)-powered portable solutions to support screening programs. This paper presents a novel approach based on a RetinaNet model with a ResNet50 backbone to detect the nuclei of cervical lesions on mobile-acquired microscopic images of cytology samples, stratifying the lesions according to The Bethesda System (TBS) guidelines. This work was supported by a new dataset of images from LBC samples digitalized with a portable smartphone-based microscope, encompassing nucleus annotations of 31,698 normal squamous cells and 1395 lesions. Several experiments were conducted to optimize the model's detection performance, namely hyperparameter tuning, transfer learning, detected class adjustments, and per-class score threshold optimization. The proposed nucleus-based methodology improved the best baseline reported in the literature for detecting cervical lesions on microscopic images exclusively acquired with mobile devices coupled to the & mu;SmartScope prototype, with per-class average precision, recall, and F1 scores up to 17.6%, 22.9%, and 16.0%, respectively. Performance improvements were obtained by transferring knowledge from networks pre-trained on a smaller dataset closer to the target application domain, as well as including normal squamous nuclei as a class detected by the model. Per-class tuning of the score threshold also allowed us to obtain a model more suitable to support screening procedures, achieving F1 score improvements in most TBS classes. While further improvements are still required to use the proposed approach in a clinical context, this work reinforces the potential of using AI-powered mobile-based solutions to support cervical cancer screening. Such solutions can significantly impact screening programs worldwide, particularly in areas with limited access and restricted healthcare resources.

Abstract
Email marketing plays a key role in business communications and is one of the most widely used applications by consumers. The literature review points to several determinants that, when applied, increase the open rate of newsletters. This research evaluates the impact of six determinants of persuasion on the opening rate of a newsletter in the hotel industry. The determinants are the day of sending, the time of sending, subject line personalization, scarcity appeal, curiosity appeal, and authority figure. The chosen methodology focused on real experiments, using a high-end luxury hotel, and the respective customer database. The newsletter was sent to the subscriber list, where one part received the control and the other part received a variant with the test version. Ten A/B tests were conducted for each determinant. The results obtained were not in line with what is indicated in the literature review. Although the literature review yielded results that showed that the application of determinants increased the open rate of newsletters, this study obtained findings to the opposite and did not confirm what was prescribed by the reviewed literature. The results of the A/B tests were conclusive and revealed that the determinants did not increase the open rate of newsletters.

Abstract
Bin picking is a challenging task involving many research domains within the perception and grasping fields, for which there are no perfect and reliable solutions available that are applicable to a wide range of unstructured and cluttered environments present in industrial factories and logistics centers. This paper contributes with research on the topic of object segmentation in cluttered scenarios, independent of previous object shape knowledge, for textured and textureless objects. In addition, it addresses the demand for extended datasets in deep learning tasks with realistic data. We propose a solution using a Mask R-CNN for 2D object segmentation, trained with real data acquired from a RGB-D sensor and synthetic data generated in Blender, combined with 3D point-cloud segmentation to extract a segmented point cloud belonging to a single object from the bin. Next, it is employed a re-configurable pipeline for 6-DoF object pose estimation, followed by a grasp planner to select a feasible grasp pose. The experimental results show that the object segmentation approach is efficient and accurate in cluttered scenarios with several occlusions. The neural network model was trained with both real and simulated data, enhancing the success rate from the previous classical segmentation, displaying an overall grasping success rate of 87.5%.

Abstract
Information security has become a necessity for all organizations. ITIL, designed for large organizations, has also been gradually adopted by smaller companies and has incorporated practices related to information security management (ISM). This study aims to understand the main risks associated with ISM, considering the context of micro companies. For this purpose, a qualitative model was built based on four case studies of micro companies in the information technology industry. The results show that companies are concerned about information security, given the growth of external threats. However, these companies have a lack of commitment, of resources, and of knowledge that hinder the implementation of an ISM policy. Therefore, it is evident that the challenge of ISM is demanding and should be addressed, considering that the security of an organization should be analyzed in a holistic context, where all perspectives should be considered to reflect the multidisciplinary nature of security.