2,568 research outputs found
On-device modeling of user's social context and familiar places from smartphone-embedded sensor data
Context modeling and recognition are crucial for adaptive mobile and
ubiquitous computing. Context-awareness in mobile environments relies on prompt
reactions to context changes. However, current solutions focus on limited
context information processed on centralized architectures, risking privacy
leakage and lacking personalization. On-device context modeling and recognition
are emerging research trends, addressing these concerns. Social interactions
and visited locations play significant roles in characterizing daily life
scenarios. This paper proposes an unsupervised and lightweight approach to
model the user's social context and locations directly on the mobile device.
Leveraging the ego-network model, the system extracts high-level, semantic-rich
context features from smartphone-embedded sensor data. For the social context,
the approach utilizes data on physical and cyber social interactions among
users and their devices. Regarding location, it prioritizes modeling the
familiarity degree of specific locations over raw location data, such as GPS
coordinates and proximity devices. The effectiveness of the proposed approach
is demonstrated through three sets of experiments, employing five real-world
datasets. These experiments evaluate the structure of social and location ego
networks, provide a semantic evaluation of the proposed models, and assess
mobile computing performance. Finally, the relevance of the extracted features
is showcased by the improved performance of three machine learning models in
recognizing daily-life situations. Compared to using only features related to
physical context, the proposed approach achieves a 3% improvement in AUROC, 9%
in Precision, and 5% in Recall
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. The dataset is released as a collection of
CSV files containing more than 45K data samples, where each sample is composed
by 1332 features related to a heterogeneous set of physical and virtual
sensors, including motion sensors, running applications, devices in proximity,
and weather conditions. Moreover, each data sample is associated with a ground
truth label that describes the user activity and the situation in which she was
involved during the sensing experiment (e.g., working, at restaurant, and doing
sport activity). To avoid introducing any bias during the data collection, we
performed the sensing experiment in-the-wild, that is, by using the volunteers'
devices, and without defining any constraint related to the user's behavior.
For this reason, the collected dataset represents a useful source of real data
to both define and evaluate a broad set of novel context-aware solutions (both
algorithms and protocols) that aim to adapt their behavior according to the
changes in the user's situation in a mobile environment
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). In this
case, users generate huge amounts of contents and they can be quickly
overloaded by useless information. At the same time, social media represent an
important source of information to characterize contents and users' interests.
RS can exploit this information to further personalize suggestions and improve
the recommendation process. In this paper we present a survey of Recommender
Systems designed and implemented for Online and Mobile Social Networks,
highlighting how the use of social context information improves the
recommendation task, and how standard algorithms must be enhanced and optimized
to run in a fully distributed environment, as opportunistic networks. We
describe advantages and drawbacks of these systems in terms of algorithms,
target domains, evaluation metrics and performance evaluations. Eventually, we
present some open research challenges in this area
Context-Aware Configuration and Management of WiFi Direct Groups for Real Opportunistic Networks
Wi-Fi Direct is a promising technology for the support of device-to-device
communications (D2D) on commercial mobile devices. 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. In fact, WiFi
Direct presents some characteristics that could limit the autonomous creation
of D2D connections among users' personal devices. Specifically, the standard
explicitly requires the user's authorization to establish a connection between
two or more devices, and it provides a limited support for inter-group
communication. In some cases, this might lead to the creation of isolated
groups of nodes which cannot communicate among each other. In this paper, we
propose a novel middleware-layer protocol for the efficient configuration and
management of WiFi Direct groups (WiFi Direct Group Manager, WFD-GM) to enable
autonomous connections and inter-group communication. This enables
opportunistic networks in real conditions (e.g., variable mobility and network
size). WFD-GM defines a context function that takes into account heterogeneous
parameters for the creation of the best group configuration in a specific time
window, including an index of nodes' stability and power levels. We evaluate
the protocol performances by simulating three reference scenarios including
different mobility models, geographical areas and number of nodes. Simulations
are also supported by experimental results related to the evaluation in a real
testbed of the involved context parameters. We compare WFD-GM with the
state-of-the-art solutions and we show that it performs significantly better
than a Baseline approach in scenarios with medium/low mobility, and it is
comparable with it in case of high mobility, without introducing additional
overhead.Comment: Accepted by the IEEE 14th International Conference on Mobile Ad Hoc
and Sensor Systems (MASS), 201
Lightweight Modeling of User Context Combining Physical and Virtual Sensor Data
The multitude of data generated by sensors available on users' mobile
devices, combined with advances in machine learning techniques, support
context-aware services in recognizing the current situation of a user (i.e.,
physical context) and optimizing the system's personalization features.
However, context-awareness performances mainly depend on the accuracy of the
context inference process, which is strictly tied to the availability of
large-scale and labeled datasets. In this work, we present a framework
developed to collect datasets containing heterogeneous sensing data derived
from personal mobile devices. The framework has been used by 3 voluntary users
for two weeks, generating a dataset with more than 36K samples and 1331
features. We also propose a lightweight approach to model the user context able
to efficiently perform the entire reasoning process on the user mobile device.
To this aim, we used six dimensionality reduction techniques in order to
optimize the context classification. Experimental results on the generated
dataset show that we achieve a 10x speed up and a feature reduction of more
than 90% while keeping the accuracy loss less than 3%
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. PLIERS is aimed at
reaching a good tradeoff between algorithmic complexity and the level of
personalization of recommended items. To evaluate PLIERS, we performed a set of
experiments on real OSN datasets, demonstrating that it outperforms
state-of-the-art solutions in terms of personalization, relevance, and novelty
of recommendations.Comment: Published in SAC '16: Proceedings of the 31st Annual ACM Symposium on
Applied Computin
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. The alert was renewed in February 2023 due to a
persisting sustained incidence of the virus in several countries and worries
about possible new outbreaks. Low-income countries with inadequate
infrastructures for vaccine and testing administration are particularly at
risk.
A symptom of mpox infection is the appearance of skin rashes and eruptions,
which can drive people to seek medical advice. A technology that might help
perform a preliminary screening based on the aspect of skin lesions is the use
of Machine Learning for image classification. However, to make this technology
suitable on a large scale, it should be usable directly on mobile devices of
people, with a possible notification to a remote medical expert.
In this work, we investigate the adoption of Deep Learning to detect mpox
from skin lesion images. The proposal leverages Transfer Learning to cope with
the scarce availability of mpox image datasets. As a first step, a homogenous,
unpolluted, dataset is produced by manual selection and preprocessing of
available image data. It will also be released publicly to researchers in the
field. Then, a thorough comparison is conducted amongst several Convolutional
Neural Networks, based on a 10-fold stratified cross-validation. The best
models are then optimized through quantization for use on mobile devices;
measures of classification quality, memory footprint, and processing times
validate the feasibility of our proposal. Additionally, the use of eXplainable
AI is investigated as a suitable instrument to both technically and clinically
validate classification outcomes.Comment: Submitted to Pervasive and Mobile Computin
An Intense and Short-Lasting Burst of Neutrophil Activation Differentiates Early Acute Myocardial Infarction from Systemic Inflammatory Syndromes
BACKGROUND: Neutrophils are involved in thrombus formation. We investigated whether specific features of neutrophil activation characterize patients with acute coronary syndromes (ACS) compared to stable angina and to systemic inflammatory diseases. METHODS AND FINDINGS: The myeloperoxidase (MPO) content of circulating neutrophils was determined by flow cytometry in 330 subjects: 69 consecutive patients with acute coronary syndromes (ACS), 69 with chronic stable angina (CSA), 50 with inflammation due to either non-infectious (acute bone fracture), infectious (sepsis) or autoimmune diseases (small and large vessel systemic vasculitis, rheumatoid arthritis). Four patients have also been studied before and after sterile acute injury of the myocardium (septal alcoholization). One hundred thirty-eight healthy donors were studied in parallel. Neutrophils with normal MPO content were 96% in controls, >92% in patients undergoing septal alcoholization, 91% in CSA patients, but only 35 and 30% in unstable angina and AMI (STEMI and NSTEMI) patients, compared to 80%, 75% and 2% of patients with giant cell arteritis, acute bone fracture and severe sepsis. In addition, in 32/33 STEMI and 9/21 NSTEMI patients respectively, 20% and 12% of neutrophils had complete MPO depletion during the first 4 hours after the onset of symptoms, a feature not observed in any other group of patients. MPO depletion was associated with platelet activation, indicated by P-selectin expression, activation and transactivation of leukocyte β2-integrins and formation of platelet neutrophil and -monocyte aggregates. The injection of activated platelets in mice produced transient, P-selectin dependent, complete MPO depletion in about 50% of neutrophils. CONCLUSIONS: ACS are characterized by intense neutrophil activation, like other systemic inflammatory syndromes. In the very early phase of acute myocardial infarction only a subpopulation of neutrophils is massively activated, possibly via platelet-P selectin interactions. This paroxysmal activation could contribute to occlusive thrombosis
Performance of CMS muon reconstruction in pp collision events at sqrt(s) = 7 TeV
The performance of muon reconstruction, identification, and triggering in CMS
has been studied using 40 inverse picobarns of data collected in pp collisions
at sqrt(s) = 7 TeV at the LHC in 2010. A few benchmark sets of selection
criteria covering a wide range of physics analysis needs have been examined.
For all considered selections, the efficiency to reconstruct and identify a
muon with a transverse momentum pT larger than a few GeV is above 95% over the
whole region of pseudorapidity covered by the CMS muon system, abs(eta) < 2.4,
while the probability to misidentify a hadron as a muon is well below 1%. The
efficiency to trigger on single muons with pT above a few GeV is higher than
90% over the full eta range, and typically substantially better. The overall
momentum scale is measured to a precision of 0.2% with muons from Z decays. The
transverse momentum resolution varies from 1% to 6% depending on pseudorapidity
for muons with pT below 100 GeV and, using cosmic rays, it is shown to be
better than 10% in the central region up to pT = 1 TeV. Observed distributions
of all quantities are well reproduced by the Monte Carlo simulation.Comment: Replaced with published version. Added journal reference and DO
Performance of CMS muon reconstruction in pp collision events at sqrt(s) = 7 TeV
The performance of muon reconstruction, identification, and triggering in CMS
has been studied using 40 inverse picobarns of data collected in pp collisions
at sqrt(s) = 7 TeV at the LHC in 2010. A few benchmark sets of selection
criteria covering a wide range of physics analysis needs have been examined.
For all considered selections, the efficiency to reconstruct and identify a
muon with a transverse momentum pT larger than a few GeV is above 95% over the
whole region of pseudorapidity covered by the CMS muon system, abs(eta) < 2.4,
while the probability to misidentify a hadron as a muon is well below 1%. The
efficiency to trigger on single muons with pT above a few GeV is higher than
90% over the full eta range, and typically substantially better. The overall
momentum scale is measured to a precision of 0.2% with muons from Z decays. The
transverse momentum resolution varies from 1% to 6% depending on pseudorapidity
for muons with pT below 100 GeV and, using cosmic rays, it is shown to be
better than 10% in the central region up to pT = 1 TeV. Observed distributions
of all quantities are well reproduced by the Monte Carlo simulation.Comment: Replaced with published version. Added journal reference and DO
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