Abstract
Wiki articles are created and maintained by a crowd of editors, producing a continuous stream of reviews. Reviews can take the form of additions, reverts, or both. This crowdsourcing model is exposed to manipulation since neither reviews nor editors are automatically screened and purged. To protect articles against vandalism or damage, the stream of reviews can be mined to classify reviews and profile editors in real-time. The goal of this work is to anticipate and explain which reviews to revert. This way, editors are informed why their edits will be reverted. The proposed method employs stream-based processing, updating the profiling and classification models on each incoming event. The profiling uses side and content-based features employing Natural Language Processing, and editor profiles are incrementally updated based on their reviews. Since the proposed method relies on self-explainable classification algorithms, it is possible to understand why a review has been classified as a revert or a non-revert. In addition, this work contributes an algorithm for generating synthetic data for class balancing, making the final classification fairer. The proposed online method was tested with a real data set from Wikivoyage, which was balanced through the aforementioned synthetic data generation. The results attained near-90% values for all evaluation metrics (accuracy, precision, recall, and F-measure).

Abstract
Background: Schizophrenia is the most common psychotic illness in the world. The negative and cognitive symptoms of this mental disorder often prevent full reintegration of patients into society, and cannot be effectively addressed with drugs alone, relying on therapy and rehabilitation. Video games as a digital tool for rehabilitation and therapy can help promote accessibility, improve patient engagement and reduce costs to institutions. Methods: A systematic review was conducted from October to November 2022 to analyze the effects of video game based rehabilitation and therapy on negative and cognitive symptoms in schizophrenic patients. The databases used to perform the search were Scopus, PubMed and Web of Science, with the search query: Schizophrenia AND (Video Game OR Serious Game). Results: A total of 228 papers were found, of which 88 duplicates were removed. After reading the titles and abstracts of the remaining 140 papers, 116 were excluded for not meeting the defined eligibility criteria for the review. Of the 24 papers left, 20 were excluded for similar reasons, resulting in the inclusion of four studies in this systematic review Conclusion: The available data for this review was limited, highlighting a need for more research in the field as well as standardization of terms used to describe the digital tools developed and assessment methods used to gather results from these interventions. Nevertheless, statistical data from the four studies included in this review showed that serious games are a promising tool for the rehabilitation and therapy of negative and cognitive symptoms of schizophrenic patients, with significant effects on the patients' performance and motivation.

Abstract
The proper integration of technology in teaching and learning processes must consider the role of teachers and students, as well as the design of tasks and the context in which they are implemented. Teachers' perceived self-efficacy significantly influences their willingness to integrate educational robotics (ER) into their practice, so initial teacher training should provide opportunities for teachers to participate in structured activities that integrate ER. In this study, a class of pre-service teachers from an initial teacher training programme were provided with their first contact with an ER platform through the use of a simulator. We present the design process of a student exploration guide and teacher guide, developed over three iterative cycles of implementation, assessment and redesign. The analysis of the data collected allowed for improvements in the design of the tasks, the graphic component of the student exploration guide, and more precise indications for the teacher's actions. The main contribution of this study is the chain orchestration between the simulator, student exploration guide and teacher guide, which allowed pre-service teachers to solve a set of challenges of increasing complexity, thereby progressively decreasing their difficulties and contributing to an adequate integration of ER in their future teaching practices.

Abstract
In this paper, we consider a demand response (DR) aggregator responsible for participating in the wholesale electricity market on behalf of the end-users who participated in the DR programs. Thus, the DR aggregator can trade its acquired DR within the short-term electricity markets, i.e., the day-ahead and the balancing (real-time) markets. In the proposed framework, the electricity market prices are considered uncertain, and a robust optimization approach is applied to address the uncertainties to maximize the profit of the DR aggregator. A model for analyzing the impact of the energy storage system (ESS) unit on a DR aggregator's performance is developed to provide more flexibility for the consumers. The direct interactions of a DR aggregator with an ESS are neglected in many models. However, this consideration can lead to improvement in the flexibility of the aggregator and also increase the profit of the entity by trading energy in the short-term markets to charge the ESS during the low-price periods and discharge it to the market while the electricity market prices are high. Hence, it is assumed that the DR aggregator owns an ESS unit and can cover a percentage of its traded power through the ESS. An analysis of the impact of the ESS unit on the DR aggregator's performance is applied to study the most appropriate size of the ESS that can maximize the profit of the aggregator. In addition, renewable energy production is employed for end-users through the installation of rooftop photovoltaic (PV) panels. This demand-side renewable generation can provide more flexibility for the participants in DR programs. Various feasible case studies have been applied to demonstrate the model's effectiveness and usefulness, and conclusions are duly drawn. The numerical results indicate that having an ESS seems necessary when the decision-maker desires to protect its profit from the worst-case scenarios and reduces the negative effect of the uncertain parameter, i.e., the wholesale electricity market prices. Thus, it can be shown that having a greater capacity for the ESS has a significant and direct impact on increasing the profit of the aggregator even in the worst-case scenarios, where the profit rises 20 % when the budget of uncertainty in the robust optimization is equal to 12.

Abstract
This work presents a novel methodology for the quality assessment of material extrusion parts through AI-based Computer Vision. To this end, different techniques are integrated using inspection methods that are applied to other areas in additive manufacturing field. The system is divided into four main points: (1) pre-processing, (2) color analysis, (3) shape analysis, and (4) defect location. The color analysis is performed in CIELAB color space, and the color distance between the part under analysis and the reference surface is calculated using the color difference formula CIE2000. The shape analysis consists of the binarization of the image using the Canny edge detector. Then, the Hu moments are calculated for images from the part under analysis and the results are compared with those from the reference part. To locate defects, the image of the part to be analyzed is first processed with a median filter, and both the original and filtered image are subtracted. Then, the resulting image is binarized, and the defects are located through a blob detector. In the training phase, a subset of parts was used to evaluate the performance of different methods and to set the values of parameters. Later, in a testing and validation phase, the performance of the system was evaluated using a different set of parts. The results show that the proposed system is able to classify parts produced by additive manufacturing, with an overall accuracy of 86.5%, and to locate defects on their surfaces in a more effective manner.

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.