@article{Rosa_2023,

title = {Evolving SPIDe Towards the Integration of Requirements Elicitation in Interaction Design},

author = {Jean C. S. Rosa and Beatriz B. Rêgo and Filipe A. Garrido and Pedro D. Valente and Nuno J. Nunes and Ecivaldo S. Matos},

url = {https://doi.org/10.5753%2Fjbcs.2023.3132},

doi = {10.5753/jbcs.2023.3132},

year  = {2023},

date = {2023-04-01},

journal = {Journal of the Brazilian Computer Society},

volume = {29},

number = {1},

pages = {19--33},

publisher = {Sociedade Brasileira de Computacao - SB},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{DBLP:journals/ijhci/PrandiNCN23,

title = {Augmenting Emerging Hospitality Services: A Playful Immersive Experience 

 to Foster Interactions among Locals and Visitors},

author = {Catia Prandi and Valentina Nisi and Chiara Ceccarini and Nuno Nunes},

year  = {2023},

date = {2023-01-01},

journal = {Int. J. Hum. Comput. Interact.},

volume = {39},

number = {2},

pages = {363--377},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{JBD2023,

title = {Using passive Wi-Fi for community crowd sensing during the COVID-19 

 pandemic},

author = {Miguel Ribeiro and Diogo Teixeira and Pedro Barbosa and Nuno Jardim Nunes},

url = {https://doi.org/10.1186/s40537-022-00675-3},

doi = {10.1186/s40537-022-00675-3},

year  = {2023},

date = {2023-12-01},

journal = {J. Big Data},

volume = {10},

number = {1},

pages = {7},

abstract = {Sensing passersby and detecting crowded locations is a growing area of research and development in the last decades. The COVID-19 pandemic compelled authorities and public and private institutions to monitor access and occupancy of crowded spaces. This work addresses the detection of crowds in points of interest (POI) by using a territory grid analysis categorizing POIs by the services available in each location and comparing data gathered from a community passive Wi-Fi infrastructure against mobile cellular tower association data from telecom companies. In Madeira islands (Portugal), we used data from the telecom provider NOS for the timespan of 4 months as ground truth and found a strong correlation with sparse passive Wi-Fi. An official regional mobile application shows the occupancy data to end-users based on the territory categorization and the passive Wi-Fi infrastructure in POIs. Occupancy data shows historical hourly trends of each location, and the real-time occupation, helping visitors and locals plan their commutes better to avoid crowded spaces.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{IJM2022,

title = {BreastScreening-AI: Evaluating medical intelligent agents for human-AI 

 interactions},

author = {Francisco Maria Calisto and Carlos Santiago and Nuno Nunes and Jacinto C. Nascimento},

url = {https://doi.org/10.1016/j.artmed.2022.102285},

doi = {10.1016/j.artmed.2022.102285},

year  = {2022},

date = {2022-12-01},

journal = {Artif. Intell. Medicine},

volume = {127},

pages = {102285},

abstract = {n this paper, we developed BreastScreening-AI within two scenarios for the classification of multimodal beast images: (1) Clinician-Only; and (2) Clinician-AI. The novelty relies on the introduction of a deep learning method into a real clinical workflow for medical imaging diagnosis. We attempt to address three high-level goals in the two above scenarios. Concretely, how clinicians: i) accept and interact with these systems, revealing whether are explanations and functionalities required; ii) are receptive to the introduction of AI-assisted systems, by providing benefits from mitigating the clinical error; and iii) are affected by the AI assistance. We conduct an extensive evaluation embracing the following experimental stages: (a) patient selection with different severities, (b) qualitative and quantitative analysis for the chosen patients under the two different scenarios. We address the high-level goals through a real-world case study of 45 clinicians from nine institutions. We compare the diagnostic and observe the superiority of the Clinician-AI scenario, as we obtained a decrease of 27% for False-Positives and 4% for False-Negatives. Through an extensive experimental study, we conclude that the proposed design techniques positively impact the expectations and perceptive satisfaction of 91% clinicians, while decreasing the time-to-diagnose by 3 min per patient.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{IJHCS2022,

title = {Modeling adoption of intelligent agents in medical imaging},

author = {Francisco Maria Calisto and Nuno Nunes and Jacinto C. Nascimento},

url = {https://doi.org/10.1016/j.ijhcs.2022.102922},

doi = {10.1016/j.ijhcs.2022.102922},

year  = {2022},

date = {2022-12-01},

journal = {Int. J. Hum. Comput. Stud.},

volume = {168},

pages = {102922},

abstract = {Artificial intelligence has the potential to transform many application domains fundamentally. One notable example is clinical radiology. A growing number of decision-making support systems are available for lesion detection and segmentation, two fundamental steps to accomplish diagnosis and treatment planning. This paper proposes a model based on the unified theory of acceptance and use of technology to study the determinants for the adoption of intelligent agents across the medical imaging workflow. We tested the model via confirmatory factor analysis and structural equation modeling using clinicians’ data from an international evaluation of healthcare practitioners. Results show an increased understanding of the vital role of security, risk, and trust in the usage intention of intelligent agents. These empirical findings provide valuable theoretical contributions to researchers by explaining the reasons behind the adoption and usage of intelligent agents in the medical imaging workflow.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Sensors2022a,

title = {A Data-Centric Analysis of the Impact of Non-Electric Data on the 

 Performance of Load Disaggregation Algorithms},

author = {João Gois and Lucas Pereira and Nuno Nunes},

url = {https://doi.org/10.3390/s22186914},

doi = {10.3390/s22186914},

year  = {2022},

date = {2022-12-01},

journal = {Sensors},

volume = {22},

number = {18},

pages = {6914},

abstract = {Recent research on non-intrusive load monitoring, or load disaggregation, suggests that the performance of algorithms can be affected by factors beyond energy data. In particular, by incorporating non-electric data in load disaggregation analysis, such as building and consumer characteristics, the estimation accuracy of consumption data may be improved. However, this association has rarely been explored in the literature. This work proposes a data-centric methodology for measuring the effect of non-electric characteristics on load disaggregation performance. A real-world dataset is considered for evaluating the proposed methodology, using various appliances and sample rates. The methodology results indicate that the non-electric characteristics may have varying effects on the performances of different building appliances. Therefore, the proposed methodology can be relevant for complementing load disaggregation analysis.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}