Abstract
Digital transformation is a phenomenon arising from social, behavioral and habitual changes due to global economic and technological development. Its main characteristic is adopting disruptive digital technologies by organizations to transform their capabilities, structures, processes and business model components. One of the disruptive digital technologies used in organizations' digital transformation process is Robotic Process Automation. However, the use of Robotic Process Automation is limited by several constraints that affect its reliability and increase the cost. Artificial Intelligence techniques can improve some of these constraints. The use of Robotic Process Automation combined with Artificial Intelligence capabilities is called Hyperautomation. However, there is a lack of solutions that successfully integrate both technologies in the context of digital transformation. This work proposes an integrated approach using Robotic Process Automation and Artificial Intelligence as disruptive Hyperautomation technology for digital transformation.

Abstract
Water scarcity is becoming an issue of more significant concern with a major impact on global sustainability. For it, new measures and approaches are urgently needed. Digital technologies and tools can play an essential role in improving the effectiveness and efficiency of current water management approaches. Therefore, a solution is proposed and validated, given the limited presence of models or technological architectures in the literature to support intelligent water management systems for domestic use. It is based on a layered architecture, fully designed to meet the needs of households and to do so through the adoption of technologies such as the Internet of Things and cloud computing. By developing a prototype and using it as a use case for testing purposes, we have concluded the positive impact of using such a solution. Considering this is a first contribution to overcome the problem, some issues will be addressed in a future work, namely, data and device security and energy and traffic optimisation issues, among several others.

Abstract
Enterprise Architecture (EA) is a coherent set of principles, methods, and models that express the structure of an enterprise and its IT landscape. EA mining uses data mining techniques to automate EA modelling tasks. Ontologies help to define concepts and the relationships among these concepts to describe a domain of interest This work presents an extensible ontology for EA mining focused on extracting architectural models that use logs from an API gateway as the data source. The proposed ontology was developed using the OntoUML language to ensure its quality and avoid anti-patterns through ontology rule checks. Then, a hypothesized scenario using data structures close to the real is used to simulate the ontology application and validate its theoretical feasibility as well as its contribution to the Enterprise Architecture Management field. Copyright © 2023 by SCITEPRESS – Science and Technology Publications, Lda. Under CC license (CC BY-NC-ND 4.0)

Abstract
An attempt has been made to address the difficulty of identifying and measuring the benefits derived from investment projects and capturing capital gains for an organization, focusing on developing and implementing a management model and realizing benefits for a leading company in its activity sector. Thus, the objective is to understand how it is possible to achieve the expected benefits of an investment project: A model characterized as generalist was developed (applied to all areas of the company), with the objective of optimizing the realization of benefits, measuring them and thus create value for the organization. Among the methods used, we highlight, in a first phase, the research of some existing Frameworks, which later enabled the development of a proposed framework, validated internally using the existing Business Intelligence platform. Subsequently, based on a satisfaction questionnaire about the framework proposed to users, data related to its development and implementation were collected, with the aim of understanding its acceptance among the users and employees of the company. With the data from this questionnaire, an artifact was developed: a PowerBI dashboard that reflects the benefits identified and captured. In summary, the artifact made it possible to identify, measure, and achieve the benefits generated by the project in question, but also to motivate its use in other existing investment projects, by adapting it to each of the other ones. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Abstract

Abstract
The purpose of this study is to describe the construction process and the evidence of content validity of SCARA, a prototype of a technological system to support language and communication rehabilitation in people with aphasia, providing a tool that serves both patients and health professionals who accompany the respective recovery process. The process followed four stages: internal phase of the program's organization, with research in the literature and analysis of the materials available in the Portuguese market; construction of the SCARA prototype; evaluation by experts; and data analysis. A Content Validity Index was calculated to determine the level of agreement between the experts. The level of agreement between experts showed the validity of SCARA. SCARA has shown to help the work of the speech-language pathologist and persons with aphasia, contributing to a higher therapeutic quality, enhancing linguistic recovery, and compensating for the impossibility of direct support more frequently and/or prolonged intervention. © 2022 ACM.