Visual analysis of sensor logs in smart spaces: Activities vs. situations

Models of human habits in smart spaces can be expressed by using a multitude of representations whose readability influences the possibility of being validated by human experts. Our research is focused on developing a visual analysis pipeline (service) that allows, starting from the sensor log of a smart space, to graphically visualize human habits. The basic assumption is to apply techniques borrowed from the area of business process automation and mining on a version of the sensor log preprocessed in order to translate raw sensor measurements into human actions. The proposed pipeline is employed to automatically extract models to be reused for ambient intelligence. In this paper, we present an user evaluation aimed at demonstrating the effectiveness of the approach, by comparing it wrt. a relevant state-of-the-art visual tool, namely SITUVIS

Surveying human habit modeling and mining techniques in smart spaces

A smart space is an environment, mainly equipped with Internet-of-Things (IoT) technologies, able to provide services to humans, helping them to perform daily tasks by monitoring the space and autonomously executing actions, giving suggestions and sending alarms. Approaches suggested in the literature may differ in terms of required facilities, possible applications, amount of human intervention required, ability to support multiple users at the same time adapting to changing needs. In this paper, we propose a Systematic Literature Review (SLR) that classifies most influential approaches in the area of smart spaces according to a set of dimensions identified by answering a set of research questions. These dimensions allow to choose a specific method or approach according to available sensors, amount of labeled data, need for visual analysis, requirements in terms of enactment and decision-making on the environment. Additionally, the paper identifies a set of challenges to be addressed by future research in the field

Your Friends Mention It. What About Visiting It? A Mobile Social-Based Sightseeing Application

In this short poster paper, we present an application for suggesting attractions to be visited by users, based on social signal processing technique

Construindo o Projeto Cuidadosamente: reflexão sobre a saúde mental dos graduandos de Enfermagem frente ao COVID-19

O presente relato de experiência visa destacar a vivência dos autores na construção do Projeto Cuidadosamente em uma universidade privada localizada no município do Rio de Janeiro. Objetiva-se com esse estudo apontar a sua inserção prática, bem como, retratar a importância de um projeto desta magnitude no cuidado à saúde psíquica dos acadêmicos de enfermagem, principalmente no contexto atual de isolamento social pela pandemia de COVID-19. Conclui-se que a ação possibilita a construção de uma rede de apoio entre os próprios colegas de classe que estão experenciando as mesmas dificuldades com esse isolamento social e ameniza situações que possam maximizar ou desencadear algum tipo de transtorno mental, a exemplo de ansiedade e depressão, através de uma escuta qualificada, que é atribuição importante do enfermeiro nos diferentes níveis de assistência. Building the Project Mindfully: reflection on the mental health of nursing students in front of COVID-19The present experience report aims to highlight the authors’ experience in the construction of the Project Mindfully in a private university located in the city of Rio de Janeiro. The objective of this study is to point out its practical insertion, as well as, to portray the importance of a project of this magnitude in the care of the psychic health of nursing students, especially in the current context of social isolation by the pandemic of COVID-19. It is concluded that the action makes it possible to build a support network among the classmates themselves who are experiencing the same difficulties with this social isolation and alleviates situations that can maximize or trigger some type of mental disorder, such as anxiety and depression, through qualified listening, which is an important role of nurses at different levels of care

HIV-1 transmitted drug resistance in newly diagnosed individuals in Italy over the period 2015-21

background: transmitted drug resistance (TDR) is still a critical aspect for the management of individuals living with HIV-1. thus, its evaluation is crucial to optimize HIV care. methods: overall, 2386 HIV-1 protease/reverse transcriptase and 1831 integrase sequences from drug-naïve individuals diagnosed in north and central Italy between 2015 and 2021 were analysed. TDR was evaluated over time. Phylogeny was generated by maximum likelihood. Factors associated with TDR were evaluated by logistic regression. Results: Individuals were mainly male (79.1%) and Italian (56.2%), with a median (IQR) age of 38 (30-48). Non-B infected individuals accounted for 44.6% (N = 1065) of the overall population and increased over time (2015-2021, from 42.1% to 51.0%, P = 0.002). TDR prevalence to any class was 8.0% (B subtype 9.5% versus non-B subtypes 6.1%, P = 0.002) and remained almost constant over time. overall, 300 transmission clusters (TCs) involving 1155 (48.4%) individuals were identified, with a similar proportion in B and non-infected individuals (49.7% versus 46.8%, P = 0.148). a similar prevalence of TDR among individuals in TCs and those out of TCs was found (8.2% versus 7.8%, P = 0.707).By multivariable analysis, subtypes A, F, and CFR02_AG were negatively associated with TDR. No other factors, including being part of TCs, were significantly associated with TDR. conclusions: between 2015 and 2021, TDR prevalence in Italy was 8% and remained almost stable over time. resistant strains were found circulating regardless of being in TCs, but less likely in non-B subtypes. these results highlight the importance of a continuous surveillance of newly diagnosed individuals for evidence of TDR to inform clinical practice

HIV-1 transmitted drug resistance in newly diagnosed individuals in Italy over the period 2015–21

Background: Transmitted drug resistance (TDR) is still a critical aspect for the management of individuals living with HIV-1. Thus, its evaluation is crucial to optimize HIV care. Methods: Overall, 2386 HIV-1 protease/reverse transcriptase and 1831 integrase sequences from drug-naïve individuals diagnosed in north and central Italy between 2015 and 2021 were analysed. TDR was evaluated over time. Phylogeny was generated by maximum likelihood. Factors associated with TDR were evaluated by logistic regression. Results: Individuals were mainly male (79.1%) and Italian (56.2%), with a median (IQR) age of 38 (30-48). Non-B infected individuals accounted for 44.6% (N = 1065) of the overall population and increased over time (2015-2021, from 42.1% to 51.0%, P = 0.002). TDR prevalence to any class was 8.0% (B subtype 9.5% versus non-B subtypes 6.1%, P = 0.002) and remained almost constant over time. Overall, 300 transmission clusters (TCs) involving 1155 (48.4%) individuals were identified, with a similar proportion in B and non-infected individuals (49.7% versus 46.8%, P = 0.148). A similar prevalence of TDR among individuals in TCs and those out of TCs was found (8.2% versus 7.8%, P = 0.707).By multivariable analysis, subtypes A, F, and CFR02_AG were negatively associated with TDR. No other factors, including being part of TCs, were significantly associated with TDR. Conclusions: Between 2015 and 2021, TDR prevalence in Italy was 8% and remained almost stable over time. Resistant strains were found circulating regardless of being in TCs, but less likely in non-B subtypes. These results highlight the importance of a continuous surveillance of newly diagnosed individuals for evidence of TDR to inform clinical practice

Visual process maps: a visualization tool for discovering habits in smart homes

Models of human habits in smart spaces can be expressed by using a multitude of representations whose readability influences the possibility of being validated by human experts. The visual analysis by domain experts allows to identify stages of human habits that could be automatized or simplified by redesigning the environment. In this paper, we present a visual analysis pipeline for graphically visualizing human habits, starting from the sensor log of a smart space,. We apply techniques borrowed from the area of business process automation and mining on a version of the sensor log preprocessed in order to translate raw sensor measurements into human actions. The proposed method is employed to automatically extract models to be reused for ambient intelligence. A user evaluation demonstrates the effectiveness of the approach, and compares it with respect to a relevant state-of-the-art visual tool, namely Situvis

Addressing multi-users open challenge in habit mining for a process mining-based approach

Models of human habits in smart spaces can be expressed by using a multitude of formalisms, whose readability influences the possibility of being validated by human experts. Given the growing availability of low-cost sensing devices promoted by the emerging Internet-of-Things, the analysis of huge amount of data produced by these systems will assume an utmost importance in the near future. But most of them are designed for single user cases. Moving forward in their development, often they hardly fit a realistic environment with many users. In this paper, we first review the most relevant approaches in the area during the last decade, and then we present an analysis pipeline that allows, starting from the sensor log of a smart space, to model human habits in a multi-user environment. The approach is based on exploit BLE beacons to discriminate the different users, then applying techniques borrowed from the area of business process automation and mining on a version of the sensor log preprocessed in order to translate raw sensor measurements into human actions. The paper also presents some hints of how the proposed method can be employed to automatically extract models to be reused for ambient intelligence in a multi-users environment