Designing websites with eXtensible web (xWeb) methodology

Today, eXtensible Markup Language (XML) is fast emerging as the dominant standard for storing, describing, representing and interchanging data among various enterprises systems and databases in the context of complex web enterprises information systems (EIS). Conversely, for web EIS (such as e-commerce and portals) to be successful, it is important to apply a high level, model driven solutions and meta-data vocabularies to design and implementation techniques that are capable of handling heterogonous schemas and documents. For this, we need a methodology that provides a higher level of abstraction of the domain in question with rigorously defined standards that are to be more widely understood by all stakeholders of the system. To-date, UML has proven itself as the language of choice for modeling EIS using OO techniques. With the introduction of XML Schema, which provides rich facilities for constraining and defining enterprise XML content, the combination of UML and XML technologies provide a good platform (and the flexibility) for modeling, designing and representing complex enterprise contents for building successful EIS. In this paper, we show how a layered view model coupled with a proven user interface analysis framework (WUiAM) is utilized in providing architectural construct and abstract website model (called eXtensible Web, xWeb), to model, design and implement simple, user-centred, collaborative websites at varying levels of abstraction. The uniqueness xWeb is that the model data (web user interface definitions, website data descriptions and constraints) and the web content are captured and represented at the conceptual level using views (one model) and can be deployed (multiple platform specific models) using one or more implementation models

Engineering XML solutions using views

In industrial informatics, engineering data intensive Enterprise Information Systems (EIS) is a challenging task without abstraction and partitioning. Further, the introduction of semi-structured data (namely XML) and its rapid adaptation by the commercial and industrial systems increased the complexity for data engineering. Conversely, the introduction of OMG's MDA presents an interesting paradigm for EIS and system modelling, where a system is designed at a higher level of abstraction. This presents an interesting problem to investigate data engineering XML solutions under the MDA initiatives, where, models and framework requires higher level of abstraction. In this paper we investigate a view model that can provide layered design methodology for modelling data intensive XML solutions for EIS paradigm, with sufficient level of abstraction

Adaptive Automated Machine Learning

The ever-growing demand for machine learning has led to the development of automated machine learning (AutoML) systems that can be used off the shelf by non-experts. Further, the demand for ML applications with high predictive performance exceeds the number of machine learning experts and makes the development of AutoML systems necessary. Automated Machine Learning tackles the problem of finding machine learning models with high predictive performance. Existing approaches incorporating deep learning techniques assume that all data is available at the beginning of the training process (offline learning). They configure and optimise a pipeline of preprocessing, feature engineering, and model selection by choosing suitable hyperparameters in each model pipeline step. Furthermore, they assume that the user is fully aware of the choice and, thus, the consequences of the underlying metric (such as precision, recall, or F1-measure). By variation of this metric, the search for suitable configurations and thus the adaptation of algorithms can be tailored to the user’s needs. With the creation of a vast amount of data from all kinds of sources every day, our capability to process and understand these data sets in a single batch is no longer viable. By training machine learning models incrementally (i.ex. online learning), the flood of data can be processed sequentially within data streams. However, if one assumes an online learning scenario, where an AutoML instance executes on evolving data streams, the question of the best model and its configuration remains open. In this work, we address the adaptation of AutoML in an offline learning scenario toward a certain utility an end-user might pursue as well as the adaptation of AutoML towards evolving data streams in an online learning scenario with three main contributions: 1. We propose a System that allows the adaptation of AutoML and the search for neural architectures towards a particular utility an end-user might pursue. 2. We introduce an online deep learning framework that fosters the research of deep learning models under the online learning assumption and enables the automated search for neural architectures. 3. We introduce an online AutoML framework that allows the incremental adaptation of ML models. We evaluate the contributions individually, in accordance with predefined requirements and to state-of-the- art evaluation setups. The outcomes lead us to conclude that (i) AutoML, as well as systems for neural architecture search, can be steered towards individual utilities by learning a designated ranking model from pairwise preferences and using the latter as the target function for the offline learning scenario; (ii) architectual small neural networks are in general suitable assuming an online learning scenario; (iii) the configuration of machine learning pipelines can be automatically be adapted to ever-evolving data streams and lead to better performances

GSI Scientific Report 2016

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Online learning of physics during a pandemic: A report from an academic experience in Italy

The arrival of the Sars-Cov II has opened a new window on teaching physics in academia. Frontal lectures have left space for online teaching, teachers have been faced with a new way of spreading knowledge, adapting contents and modalities of their courses. Students have faced up with a new way of learning physics, which relies on free access to materials and their informatics knowledge. We decided to investigate how online didactics has influenced students’ assessments, motivation, and satisfaction in learning physics during the pandemic in 2020. The research has involved bachelor (n = 53) and master (n = 27) students of the Physics Department at the University of Cagliari (N = 80, 47 male; 33 female). The MANOVA supported significant mean differences about gender and university level with higher values for girls and master students in almost all variables investigated. The path analysis showed that student-student, student-teacher interaction, and the organization of the courses significantly influenced satisfaction and motivation in learning physics. The results of this study can be used to improve the standards of teaching in physics at the University of Cagliar