Search Results for author:
Found 14 papers, 3 papers with code
ContextLabeler Dataset: physical and virtual sensors data collected from smartphone usage in-the-wild
This paper describes a data collection campaign and the resulting dataset derived from smartphone sensors characterizing the daily life activities of 3 volunteers in a period of two weeks.
Context-Aware Configuration and Management of WiFi Direct Groups for Real Opportunistic Networks
However, the standard as-it-is is not sufficient to support the real deployment of networking solutions entirely based on D2D such as opportunistic networks.
Transfer Learning for the Efficient Detection of COVID-19 from Smartphone Audio Data
The efficacy of this solution mainly depends on the performances of AI algorithms applied to the collected data and their possible implementation directly on the users' mobile devices.
PLIERS: a Popularity-Based Recommender System for Content Dissemination in Online Social Networks
In this paper, we propose a novel tag-based recommender system called PLIERS, which relies on the assumption that users are mainly interested in items and tags with similar popularity to those they already own.
A Machine-Learned Ranking Algorithm for Dynamic and Personalised Car Pooling Services
Car pooling is expected to significantly help in reducing traffic congestion and pollution in cities by enabling drivers to share their cars with travellers with similar itineraries and time schedules.
Recommender Systems for Online and Mobile Social Networks: A survey
Recommender Systems (RS) currently represent a fundamental tool in online services, especially with the advent of Online Social Networks (OSN).
Lightweight Modeling of User Context Combining Physical and Virtual Sensor Data
The framework has been used by 3 voluntary users for two weeks, generating a dataset with more than 36K samples and 1331 features.
MyDigitalFootprint: an extensive context dataset for pervasive computing applications at the edge
To demonstrate the dataset's effectiveness, we present three context-aware applications utilizing various machine learning tasks: (i) a social link prediction algorithm based on physical proximity data, (ii) daily-life activity recognition using smartphone-embedded sensors data, and (iii) a pervasive context-aware recommender system.
On-device modeling of user's social context and familiar places from smartphone-embedded sensor data
These experiments evaluate the structure of social and location ego networks, provide a semantic evaluation of the proposed models, and assess mobile computing performance.
Explainable AI for Malnutrition Risk Prediction from m-Health and Clinical Data
This paper presents a novel AI framework for early and explainable malnutrition risk detection based on heterogeneous m-health data.
A Transfer Learning and Explainable Solution to Detect mpox from Smartphones images
In recent months, the monkeypox (mpox) virus -- previously endemic in a limited area of the world -- has started spreading in multiple countries until being declared a ``public health emergency of international concern'' by the World Health Organization.
Explainable AI for clinical and remote health applications: a survey on tabular and time series data
Nowadays Artificial Intelligence (AI) has become a fundamental component of healthcare applications, both clinical and remote, but the best performing AI systems are often too complex to be self-explaining.
On-device modeling of user's social context and familiar places from smartphone-embedded sensor data
By using 5 real-world datasets, we assess the structure of the social and location ego networks, we provide a semantic evaluation of the proposed models and a complexity evaluation in terms of mobile computing performance.
L3-Net Deep Audio Embeddings to Improve COVID-19 Detection from Smartphone Data
Smartphones and wearable devices, along with Artificial Intelligence, can represent a game-changer in the pandemic control, by implementing low-cost and pervasive solutions to recognize the development of new diseases at their early stages and by potentially avoiding the rise of new outbreaks.
