Abstract
Over the years, several systematic literature reviews have been published reporting advances in tools and techniques for automated assessment in Computer Science. However, there is not yet a major bibliometric study that examines the relationships and influence of publications, authors, and journals to make these research trends visible. This paper presents a bibliometric study of automated assessment of programming exercises, including a descriptive analysis using various bibliometric measures and data visualizations. The data was collected from the Web of Science Core Collection. The obtained results allow us to identify the most influential authors and their affiliations, monitor the evolution of publications and citations, establish relationships between emerging themes in publications, discover research trends, and more. This paper provides a deeper knowledge of the literature and facilitates future researchers to start in this field.

Abstract
Unmanned Aerial Vehicles (UAVs) have emerged as adequate platforms to carry communications nodes, including Wi-Fi Access Points and cellular Base Stations. This has led to the concept of flying networks composed of UAVs as a flexible and agile solution to provide on-demand wireless connectivity anytime, anywhere. However, state of the art works have been focused on optimizing the placement of the access network providing connectivity to ground users, overlooking the backhaul network design. In order to improve the overall Quality of Service (QoS) offered to ground users, the placement of Flying Gateways (FGWs) and the size of the queues configured in the UAVs need to be carefully defined to meet strict performance requirements. The main contribution of this article is a traffic-aware gateway placement and queue management (GPQM) algorithm for flying networks. GPQM takes advantage of knowing in advance the positions of the UAVs and their traffic demand to determine the FGW position and the queue size of the UAVs, in order to maximize the aggregate throughput and provide stochastic delay guarantees. GPQM is evaluated by means of ns-3 simulations, considering a realistic wireless channel model. The results demonstrate significant gains in the QoS offered when GPQM is used.

Abstract
Data clustering combined with multiobjective optimization has become attractive when the structure and the number of clusters in a dataset are unknown. Data clustering is the main task of exploratory data mining and a standard statistical data analysis technique used in many fields, including machine learning, pattern recognition, image analysis, information retrieval, and bioinformatics. This project analyzes data to extract possible failure patterns in Solar Photovoltaic (PV) Panels. When managing PV Panels, preventive maintenance procedures focus on identifying and monitoring potential equipment problems. Failure patterns such as soiling, shadowing, and equipment damage can disturb the PV system from operating efficiently. We propose a multiobjective evolutionary algorithm that uses different distance functions to explore the conflicts between different perspectives of the problem. By the end, we obtain a non-dominated set, where each solution carries out information about a possible clustering structure. After that, we pursue a-posteriori analysis to exploit the knowledge of non-dominated solutions and enhance the fault detection process of PV panels.

Abstract
[No abstract available]

Abstract
Immersive learning research is a field of study that emphasizes diversity of scholarship and subject areas. This diversity presents a challenge for understanding the breadth and depth of the field of immersive learning, a challenge that led to the Immersive Learning Research Network’s call for the community of immersive learning researchers to develop a conceptual framework supporting a common understanding of this diverse field - The Immersive Learning Knowledge Tree. However, this structure has not had its underlying assumptions validated by the larger, diverse community of immersive learning researchers and practitioners. Thus, we developed, validated, and disseminated across associations of the field a questionnaire for analyzing the assumptions, structure, and relevance of the Knowledge Tree proposal. Early results point towards overwhelming agreement from the community on the premise that the field of immersive learning research is muddled/fragmented, the current knowledge partially disjointed, specifically among different disciplines (Q3), due to its interdisciplinary nature. There are also strong indications supporting the premise that researchers active in the field of immersive learning research desire to combine their efforts with others.

Abstract
Recently, deep learning has proven to be a successful technique especially in medical image analysis. This paper aims to highlight the importance of deep learning architectures in lung disease diagnosis using CXR images. Related articles were identified through searches of electronic resources, including IEEE, Springer, Elsevier, PubMed, Nature and, Hindawi digital library. The inclusion of articles was based on high-performance artificial intelligence models, developed for the classification of possible findings in CXR images published from 2018 to 2023.After the quality assessment of papers, 129 articles were included according to PRISMA guidelines. Papers were studied by types of lung disease, data source, algorithm type, and outcome metrics. Three main categories of computer-aided lung disease detection were covered: traditional machine learning, deep learning-based methods, and combination of aforementioned methods for all lung diseases.The results showed that various pre-trained networks including ResNet, VGG, and DenseNet, are the most frequently used CNN architectures and would result in a notable increase in sensitivity and accuracy. Recent research suggests that utilizing a combination of deep networks with a robust machine learning classifier can outperform deep learning approaches that rely solely on fully connected neural networks as their classifier. Finally, the limitations of the existing literature and potential future research opportunities in possible findings in CXR images using deep learning architectures are discussed in this systematic review.