Abstract
The demand for high-performance solutions for anomaly detection and forecasting fault events is increasing in the industrial area. The detection and forecasting faults from time-series data are one critical mission in the Internet of Things (IoT) data mining. The classical fault detection approaches based on physical modelling are limited to some measurable output variables. Accurate physical modelling of vehicle dynamics requires substantial prior information about the system. On the other hand, data-driven modelling techniques accurately represent the system's dynamic from data collection. Experimental results on large-scale data sets from Metro do Porto subsystems verify that our method performs high-quality fault detection and forecasting solutions. Also, health indicator obtained from the principal component analysis of the forecasting solution is applied to predict the remaining useful life.

Abstract
The future is envisaged to have renewable energy resources replacing conventional sources of energy like fossil fuels. In this direction wind and solar energy is emerging to be a vital source of green energy. Although these resources are a promising aspect in providing clean and cheap electrical energy, one demerit is that it is intermittent and therefore unpredictable. This intermittent nature poses a challenge in maintaining the balance between generation and demand of electrical energy thus adversely affecting the system control. Also, the electrical energy companies involved in selling by participating in the electricity pool market need highly accurate solar and wind energy predictions for maximizing their profit. These issues demand a tool for accurate prediction of generation. This paper proposes machine learning prediction models for wind and solar irradiation. For this, a case study is done considering weather data of Malviya National Institute of Technology in Jaipur used to train the regression models. The best-trained model is tested with unseen data and shown to have reasonably good accuracy in predicting wind speed and solar irradiation. A comparative study of regression model performances is done. It is shown that Gaussian Process Regression-based prediction for solar irradiation and the Support Vector Machine outperforms the trained model for the wind speed predictions. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Abstract
The widespread use of smartphones and other low-cost equipment as recording devices, the massive growth in bandwidth, and the ever-growing demand for new applications with enhanced capabilities, made visual data a must in several scenarios, including surveillance, sports, retail, entertainment, and intelligent vehicles. Despite significant advances in analyzing and extracting data from images and video, there is a lack of solutions able to analyze and semantically describe the information in the visual scene so that it can be efficiently used and repurposed. Scientific contributions have focused on individual aspects or addressing specific problems and application areas, and no cross-domain solution is available to implement a complete system that enables information passing between cross-cutting algorithms. This paper analyses the problem from an end-to-end perspective, i.e., from the visual scene analysis to the representation of information in a virtual environment, including how the extracted data can be described and stored. A simple processing pipeline is introduced to set up a structure for discussing challenges and opportunities in different steps of the entire process, allowing to identify current gaps in the literature. The work reviews various technologies specifically from the perspective of their applicability to an end-to-end pipeline for scene analysis and synthesis, along with an extensive analysis of datasets for relevant tasks.

Abstract
In recent years, the research community has raised serious questions about the replicability and reproducibility of scientific work. In particular, since many studies include some kind of computing work, these are also technological challenges, not only in computer science but in most research domains. Replicability and reproducibility are not easy to achieve, not only because researchers have diverse proficiency in computing technologies, but also because of the variety of computational environments that can be used. Indeed, it is challenging to recreate the same environment using the same frameworks, code, programming languages, dependencies, and so on. In this work, we propose a vision for an Integrated Development Environment allowing the creation, configuration, execution, packaging, and sharing of scientific computational experiments. Such a framework should allow researchers to easily set the code and data used and define the programming languages, code, dependencies, databases, or commands to execute to achieve consistent results for each experiment. With this work, we intend to aid researchers by integrating into the same platform all the stages of the design, execution, and analysis of a computational experiment.

Abstract
Computer architecture is a prevalent topic of study in Informatics and Electrical Engineering courses, though students’ overall grasp of this subject’s concepts is many times hampered, mainly due to the lack of educational tools that can intuitively represent the internal behaviour of a CPU. With the evolution of the ARM architecture and its adoption in higher education institutions, the demand for this sort of tool has increased. Educational tools, specifically developed for the ARMv8 processor, are scarce and inadequate for what is necessary in an academic context. In order to contribute towards solving this problem, eduARM, a practical and interactive web platform that simulates how a ARMv8 CPU functions, was developed and is presented through this paper. Since this tool’s main purpose is to aid computer architecture students, contributing to an improvement in their learning experience, it comprises varied concepts of computer architecture and organization in a simple and intuitive manner, such as the internal structure of a CPU, in both its unicycle and pipelined versions, and the effects of executing a set of instructions. As to better understand its value, the developed tool was then validated through a case study with the participation of computer architecture students. Copyright © 2023 by SCITEPRESS – Science and Technology Publications, Lda. Under CC license (CC BY-NC-ND 4.0)

Abstract
Early detection of melanoma is crucial for preventing severe complications and increasing the chances of successful treatment. Existing deep learning approaches for melanoma skin lesion diagnosis are deemed black-box models, as they omit the rationale behind the model prediction, compromising the trustworthiness and acceptability of these diagnostic methods. Attempts to provide concept-based explanations are based on post-hoc approaches, which depend on an additional model to derive interpretations. In this paper, we propose an inherently interpretable framework to improve the interpretability of concept-based models by incorporating a hard attention mechanism and a coherence loss term to assure the visual coherence of concept activations by the concept encoder, without requiring the supervision of additional annotations. The proposed framework explains its decision in terms of human-interpretable concepts and their respective contribution to the final prediction, as well as a visual interpretation of the locations where the concept is present in the image. Experiments on skin image datasets demonstrate that our method outperforms existing black-box and concept-based models for skin lesion classification. © 2023 IEEE.