Specialisation in Information Systems
DOCIS-2024-01: Virtual and Augmented Reality
Virtual and Augmented Reality is a research topic that studies the multi-layers of our technology-surrounded reality and their increasingly frequent interlacing. Besides the technological hype and its still tangible limitations, Virtual Reality and Augmented Reality require new research to experiment and propose human-centred interfaces and natural gateways between the different realities. At USJ, we are interested in topics such as Arts in Mixed-Reality, User Experience and User Interfaces within Extended Reality realms.
Principal Supervisor: Gerald Estadieu (gestadieu@usj.edu.mo)
Academic Unit: Institute for Data Engineering and Science
Keywords: No keywords available
DOCIS-2024-02: Human-Computer Interaction (HCI)
Human-Computer Interaction (HCI) is a field of study that delves into the interactions between users and digital products/systems. It employs methodological frameworks and practice-based experimentations to analyse human cognitive actions and reactions towards digital interfaces. The primary goal of HCI is to create digital products that enable users to achieve their objectives in the most effective manner. At USJ, our research focuses on Human-Centered Interaction, Digital and Tangible products, aiming to enhance the user experience and usability of digital interfaces.
Principal Supervisor: Filipa Martins (filipa.martins@usj.edu.mo)
Academic Unit: Institute for Data Engineering and Science
Keywords: No keywords available
DOCIS-2024-03: Digital Humanities
Digital Humanities (DH) sit at the crossroads of computer science and the humanities. Since the early time of computing, a wide range of computational tools have enabled humanities scholars to conduct research at a scale once thought impossible. Digital humanities foster collaboration and traverse disciplines and methodological orientations. This research field employs technology in pursuing humanities research and subjects technology to humanistic questioning, often simultaneously. At USJ, we aim to develop digital tools and computational methods to enrich the humanities, arts, and social sciences.
Principal Supervisor: Carlos Caires (carlos.caires@usj.edu.mo)
Academic Unit: Institute for Data Engineering and Science
Keywords: No keywords available
DOCIS-2024-04: Sound and Music Computing
Sound and music computing (SMC) is a research topic that studies sound and music communication from a multidisciplinary point of view. It combines scientific, technological and artistic methodologies to understand, model, process, and generate sound and music through computational approaches. At USJ, we focus on topics that include (non-exclusively) Soundscapes, Sound Design, Music Creativity and Technology, Computational Psychoacoustics, Film Sound, Digital Music Instruments Design and Prototyping, Sonic Interaction Design, Sonification, Auditory Display, and Networked Audio.
Principal Supervisor: Alvaro Barbosa (abarbosa@usj.edu.mo), Gerald Estadieu (gestadieu@usj.edu.mo)
Academic Unit: Institute for Data Engineering and Science
Keywords: No keywords available
DOCIS-2024-05: Open Distance Digital Learning Environment (virtual campus)
Innovative solutions in Open Distance Digital Learning aim to provide students with high levels of learning flexibility through an e-learning platform, embracing intelligent, affective, empathetic, and engaging interfaces for various university educational scenarios that share a common virtual campus, bringing together a global teaching-learning community.
At USJ, we concentrate our research on identifying improvements in learner-learner, teacher-learner, and machine-human interactions, content quality, as well as educational management and analytics, enabling students to learn anywhere and at any time, regardless of the spatial and temporal constraints that traditional classroom teaching imposes. This approach fosters enhanced scenarios for online asynchronous collaboration within the learning community. Thus, our research encompasses not only the design and experimental technical development of a global virtual campus but also the conception of appropriate pedagogical mechanisms and strategies.
Main research question: How can a global online teaching-learning community be gradually implemented and supported throughout a virtual campus?
Principal Supervisor: Adérito Marcos (aderito.marcos@usj.edu.mo)
Academic Unit: Institute for Data Engineering and Science
Keywords: Open Distance Teaching-Learning, Global Online Teaching-Learning Community, E-learning Platforms, Pedagogical Models, Virtual Campus
DOCIS-2024-06: Hybrid Immersive Media Technologies
Hybrid immersive media is an emerging field that combines traditional disciplines of handmade technical and perspective drawing with modern VR and AR visualisation techniques. It focuses on exploring hybrid interfaces between the physical and digital, with applications in architectural ideation and design, the documentation and dissemination of cultural heritage, and commercial ventures in tourism. Software and hardware tools are still in the initial stages in this field, leaving plenty of opportunity for technological and conceptual advancements.
At USJ, we are keen to engage in the research, conceptualisation, and development of these tools through collaborative efforts between PhD candidates specialised in software development and experts in illustration, architecture, and design.
Principal Supervisor: Adérito Marcos (aderito.marcos@usj.edu.mo), António Araújo (antonio.araujo@uab.pt)
Academic Unit: Institute for Data Engineering and Science
Keywords: Hybrid Immersive Media, Virtual and Augmented Reality, VR and AR Visualisation, Hybrid Interfaces, Cultural Heritage
DOCIS-2024-07: Image and Signal Processing
Image and Signal processing are two of the most promising areas in Information Systems and Data Sciences with multiple applications, such as health, environment and finance. The research possibilities are vast and relevant. From the image processing point of view, intelligent computational vision applications to medical imaging, including healthcare applications, while the variety of techniques and approaches is increasing daily. The trends are related to image acquisition and high-quality image processing; 2D capture evolving to 3D and AI supplemental systems for image analysis. From the signal processing side, time series analysis based on linear and nonlinear approaches is a key trend, including biosignals, financial time series, prediction tools, etc. Machine Learning approaches and Deep Learning algorithms and models are a core area of research for both, including processing and analysing signals from brain-computer interfaces (BCI).
Principal Supervisor: Alexandre Lobo (alexandre.lobo@usj.edu.mo)
Academic Unit: Institute for Data Engineering and Science
Keywords: No keywords available
DOCIS-2024-08: Community Health Analytics
The ability to mine community health data and identify critical health-related determinants to inform public health decisions effectively is crucial to improving the health and lives of people around the world. Challenges to tackle the leading causes of morbidity and mortality of infectious and chronic diseases, accompanied by the rising demand for mobile and home-based devices in an ever-changing technological world, pave the way to personalised healthcare service and delivery in the coming decade. The data generated from these said devices are essential to healthcare data analytics, mainly when employed by Big Data Intelligence, Machine Learning, and the Internet of Things Systems. This project is to explore viable and efficient data analytics models that could assist in data assimilation, data accessibility, and real-time outcome dissimilation, in addition to evaluating the best practice of community health data analytics that could establish a more pragmatic interpretation of data for the community health improvement.
Principal Supervisor: Jacky Ho (jackyh@usj.edu.mo)
Academic Unit: Institute for Data Engineering and Science
Keywords: No keywords available
DOCIS-2024-10: Social Media Sentiment Analysis
The rapid development of the Internet and mobile devices, especially the upsurge of Web 2.0, has enabled the emergence of social media platforms that led to the creation of the field of research called ‘sentiment analysis’ to analyse this large-scale online resource of unstructured opinions. Sentiment analysis makes use of text analytics to capture public opinion. It is one of the most appealing use cases of Natural Language Processing (NLP), with interest from both industry and academia. Research nowadays embraces sentence-level subjectivity detection, aspect-based sentiment analysis, sentiment analysis on figurative language, topic-based polarity classification, classification of the implicit polarity of events, i.e. recognising “pleasant” or “unpleasant” circumstances when no straight polarity marker is mentioned, emotion classification, and more recently, stance detection and argument mining, all on a wide variety of languages and diversified media. Task definitions have evolved towards more complex challenges where subjectivity, polarity recognition, and opinion mining have enriched fine-grained aspects and topic-level predictions. Research challenges nowadays cover the following main topics: a) Aspect Based Sentiment Analysis, aimed at capturing the sentiments expressed on different aspects of entities such as products, movies, companies, etc., on user-generated comments; b) Emotion Analysis, aiming at detecting and recognising types of feelings in texts, such as anger, disgust, fear, happiness, sadness, and surprise; c) Spam and Fake Detection: to detect and analyse fake reviews and fake news which are closely related phenomena, as both consist of writing and spreading false information or beliefs, looking for efficient ways to tell the difference between an honest review and a fake one; d) Multimodal Sentiment Analysis, aiming at integrating into research the different social multimedia as multi-modal sentiment analysis that will bring new opportunities by embracing other, complementary data streams such as facial and vocal displays and expressions that express sentiment often in a compelling way.
Principal Supervisor: George Du (george.du@usj.edu.mo)
Academic Unit: Institute for Data Engineering and Science
Keywords: No keywords available
DOCIS-2024-11: Technology Acceptance Studies
New technologies (e.g., autonomous electric vehicles, internet of things (IoT) products, smart home systems, wearable devices, blockchain applications, etc.) are reshaping everyday life. But why are some technologies spreading faster than others? Some innovations have struggled to gain acceptance in society, even if they are arguably better than the current ones. Diffusion of Innovations (Rogers, 1962), Technology Acceptance Model (TAM; Davis, 1986), and Unified Theory of Acceptance and Use of Technology (UTAUT; Venkatesh et al., 2003) are well-accepted frameworks that have attempted to explain why individuals accept and use innovations, however many areas of research are still lacking. At USJ, the research's primary goal is to apply established models to study how individuals or organisations accept and use emerging technologies by applying quantitative and statistical tools (i.e., structural equation modelling) to analyse the collected data.
Principal Supervisor: Alessandro Lampo (lampo.alessandro@usj.edu.mo)
Academic Unit: Institute for Data Engineering and Science
Keywords: No keywords available
DOCIS-2024-12: Healthcare Wearable Data Streams Intelligent Analysis
The elderly care solutions revolve around the home safety of the elderly, life care, abnormality monitoring, and timely treatment. Regarding medical treatment, health, security, and life enhancement for the elderly, wearable technology has stepped forward immensely into smart elderly care, interacting with people in every aspect and shaping the elder’s new lifestyle that technology brings. Wearable technology for healthcare can be defined as one that has intelligence and can give and receive input and provide meaningful output. It provides a valuable monitoring tool for healthcare. For some time, it has brought us unique challenges since wearable devices and electronics for healthcare present a unique interface of technology and person that needs to account both for technological and personal aspects of the problem. The following aspects may be considered as some of the challenges facing the field of healthcare wearables:
The challenge of modern wearable electronics in health and wellness needs to be supported by extensions of sensing and data analytics capabilities. Thus, the fundamental research topic is how to efficiently and informatively interpret the data generated by wearable devices. In other words, the research topic focuses on the interpretation of such data streams and connect with health outcomes, using sensor data to guide behavioural interventions, and using medical data mining and machine learning techniques to obtain valuable insights into and patterns of elderly patient data for disease prediction, emergency detection and regulation of elderly health systems.
Principal Supervisor: George Du (george.du@usj.edu.mo)
Academic Unit: Institute for Data Engineering and Science
Keywords: No keywords available
DOCIS-2024-13: Artificial Intelligence-based Healthcare and Geriatrics
With the rapid development of artificial intelligence, artificial intelligence is affecting the field of healthcare from many aspects such as diagnosis, treatment, follow-up and so on.
PhD research topic of Artificial Intelligence-based Healthcare and Geriatrics will enable future academic, clinical, industry, and government leaders to rapidly transform patient care, improve health equity and outcomes, and accelerate precision medicine by creating new AI technologies that reason across massive-scale biomedical data and knowledge.
Purpose: Students will gain an unparalleled understanding and appreciation of how their research will tangibly impact health care and patient well-being. This approach aims to enhance innovation between the fields of statistics, computer science, bioinformatics, artificial intelligence, epidemiology, and clinical medicine to achieve a much-needed change in healthcare.
PhD research topic of Artificial Intelligence-based Healthcare and Geriatrics will train exceptional, computationally minded students how to solve problems in the context of Healthcare and Geriatrics using AI. While there are no specific background requirements, the track is, by necessity, quantitatively rigorous. Therefore, successful applicants will show a mastery of fields such as statistics, linear algebra, computer science, and machine learning.
Principal Supervisor: George Du (george.du@usj.edu.mo)
Academic Unit: Institute for Data Engineering and Science
Keywords: No keywords available
DOCIS-2024-14: Data-driven Modeling
The topic of Data-driven Modeling at USJ considers its degree programs as transdisciplinary, intended for those who wish to pursue a career in the fields of academia, government, healthcare/medicine, entertainment, technology, education, or service. Most engineering or scientific fields use Data-driven Modeling as an exploration or analysis technique. However, data-driven modelling is not limited to engineering and science; it is also used in training, management, and concept exploration. These programs involve constructing human-centred, equipment-centred, and stand-alone computer-based models of existing and conceptual systems and processes, where doctoral students working on the area of Data-driven Modeling must focus their study and research efforts on one of these specific fields.
Principal Supervisor: George Du (george.du@usj.edu.mo)
Academic Unit: Institute for Data Engineering and Science
Keywords: No keywords available
DOCIS-2024-15: Biocompatible Communications in Healthcare Wearables
The elderly care solutions revolve around the home safety of the elderly, life care, abnormality monitoring, and timely treatment. Regarding medical treatment, health, security, and life enhancement for the elderly, wearable technology has stepped forward immensely into smart elderly care, interacting with the elder in every aspect and shaping the elder’s new lifestyle that technology brings. Wearable technology for healthcare can be defined as one that has intelligence and can give and receive input and provide meaningful output. It provides a valuable monitoring tool for healthcare. For some time, it has brought us unique challenges since wearable devices and electronics for healthcare present a unique interface of technology and person that needs to account both for technological and personal aspects of the problem. Communications from the body to the outside world and communications among multiple wearables on the body demand new solutions. Traditional radio methods experience challenges due to absorption by body tissue. The related challenges include the development of efficient methods for communicating through or on the body, including the organisation of wearables in body sensor networks and their integration into the Internet of Things. We can explore the following issues: 1) Can a future wearable communicate with other smartphones and tablets? 2) Can we consider the elder’s body as the communication medium for exchanging information with another wearable?
Principal Supervisor: George Du (george.du@usj.edu.mo)
Academic Unit: Institute for Data Engineering and Science
Keywords: No keywords available
DOCIS-2024-16: Business Processes Automation (A)
Robotic Process Automation (RPA) tools have become entrenched in nearly every sector, with their use marking the most significant digital transformation in business processes over the past decade. Although numerous frameworks and a wealth of case studies support their adoption and implementation, there remain gaps in other areas that require further research.
Several tools are available on the market, each with varying maturity levels and functionalities. This means that some tools are more effective in certain situations than others. An organisation may be led to adopt and implement process automation based on various solutions from different vendors. However, this creates the challenge of process orchestration, where some processes are run by one tool and others by another.
Research question: How can processes automated by different RPA tools be orchestrated?
Principal Supervisor: Adérito Marcos (aderito.marcos@usj.edu.mo), José Henrique Pereira São Mamede (jose.mamede@uab.pt)
Academic Unit: Institute for Data Engineering and Science
Keywords: Business Processes Automation, Process Orchestration, Robotic Process Automation, Digital Transformation, Business Processes
DOCIS-2024-17: Business Processes Automation (B)
Robotic Process Automation (RPA) tools have become entrenched in nearly every sector, constituting the most significant digital transformation in business processes over the last decade. While numerous frameworks and various case studies advocate for their adoption and implementation, further research is essential to address gaps in other areas.
Utilising diverse RPA tools from different vendors does not foster the development of a knowledge base that encourages increasingly standardised processes rooted in best practices. Conversely, if such a knowledge base could serve as a repository for process frameworks, process automation could be significantly enhanced and optimised.
Research Question: What framework can be proposed to establish a knowledge repository for business processes optimised for automation, irrespective of the tool technology?
Principal Supervisor: Adérito Marcos (aderito.marcos@usj.edu.mo), José Henrique Pereira São Mamede (jose.mamede@usj.edu.mo)
Academic Unit: Institute for Data Engineering and Science
Keywords: Business Processes Automation, Robotic Process Automation, Repository of Process Skeletons, Knowledge Repository, Digital Transformation
DOCIS-2024-18: Enterprise Architecture and Digital Transformation
The practice of Enterprise Architecture (EA) has extensively supported organisations in their digital transformation processes. However, these transformation processes must encompass cybersecurity and artificial intelligence considerations. This approach can create cybersecure and intelligent organisational ecosystems.
However, focusing on the most widely used EA framework, TOGAF (The Open Group Architecture Framework), none of these concepts are intrinsic to it, making it challenging to ensure that the 2 aspects mentioned are always present.
Research question: What extension of TOGAF is needed to accommodate cybersecurity and artificial intelligence when mapping architectures from as-is to to-be?
Principal Supervisor: Adérito Marcos (aderito.marcos@usj.edu.mo), José Henrique Pereira São Mamede (jose.mamede@uab.pt)
Academic Unit: Institute for Data Engineering and Science
Keywords: Enterprise Architecture, Digital Transformation, Cybersecurity, Artificial Intelligence, Transformation Processes
DOCIS-2025-01: Semantic and Generative Storytelling Systems
The development of tools that generate relevant semantic connections within large bodies of audiovisual material can support new interactive artefacts. Exploring the development of authoring tools that support authors in developing interactive media with dedicated systems that support custom taxonomies should expand the ways of thinking about interactive narratives and allow the democratisation of these formats, which generally require expensive dedicated teams of programmers.
These systems are already used in both fictional and documentary filmmaking. They may support new, engaging ways for people to access and perceive large amounts of information or complex connections between topics, events or personalities.
The challenge in these research areas is not only in developing the systems but also in assessing their potential impact on users and/or audiences.
The relevance of research in this field stems from merging different fields, such as narrative, computing, and interface design, into one specific line of research.
Principal Supervisor: António Carvalho Maneira (antonio.maneira@usj.edu.mo), Adérito Marcos (aderito.marcos@usj.edu.mo)
Academic Unit: Institute for Data Engineering and Science
Keywords: Digital Storytelling, Semantic and Generative Storytelling Systems, Interactive Artefacts, Interactive Media
