41 research outputs found
ARTEMIS: Real-Time Detection and Automatic Mitigation for BGP Prefix Hijacking
Prefix hijacking is a common phenomenon in the Internet that often causes
routing problems and economic losses. In this demo, we propose ARTEMIS, a tool
that enables network administrators to detect and mitigate prefix hijacking
incidents, against their own prefixes. ARTEMIS is based on the real-time
monitoring of BGP data in the Internet, and software-defined networking (SDN)
principles, and can completely mitigate a prefix hijacking within a few minutes
(e.g., 5-6 mins in our experiments) after it has been launched
14 Years of Self-Tracking Technology for mHealth -- Literature Review: Lessons Learnt and the PAST SELF Framework
In today's connected society, many people rely on mHealth and self-tracking
(ST) technology to help them adopt healthier habits with a focus on breaking
their sedentary lifestyle and staying fit. However, there is scarce evidence of
such technological interventions' effectiveness, and there are no standardized
methods to evaluate their impact on people's physical activity (PA) and health.
This work aims to help ST practitioners and researchers by empowering them with
systematic guidelines and a framework for designing and evaluating
technological interventions to facilitate health behavior change (HBC) and user
engagement (UE), focusing on increasing PA and decreasing sedentariness. To
this end, we conduct a literature review of 129 papers between 2008 and 2022,
which identifies the core ST HCI design methods and their efficacy, as well as
the most comprehensive list to date of UE evaluation metrics for ST. Based on
the review's findings, we propose PAST SELF, a framework to guide the design
and evaluation of ST technology that has potential applications in industrial
and scientific settings. Finally, to facilitate researchers and practitioners,
we complement this paper with an open corpus and an online, adaptive
exploration tool for the PAST SELF data.Comment: 40 pages, 10 figure
Uncovering Bias in Personal Informatics
Personal informatics (PI) systems, powered by smartphones and wearables,
enable people to lead healthier lifestyles by providing meaningful and
actionable insights that break down barriers between users and their health
information. Today, such systems are used by billions of users for monitoring
not only physical activity and sleep but also vital signs and women's and heart
health, among others. %Despite their widespread usage, the processing of
particularly sensitive personal data, and their proximity to domains known to
be susceptible to bias, such as healthcare, bias in PI has not been
investigated systematically. Despite their widespread usage, the processing of
sensitive PI data may suffer from biases, which may entail practical and
ethical implications. In this work, we present the first comprehensive
empirical and analytical study of bias in PI systems, including biases in raw
data and in the entire machine learning life cycle. We use the most detailed
framework to date for exploring the different sources of bias and find that
biases exist both in the data generation and the model learning and
implementation streams. According to our results, the most affected minority
groups are users with health issues, such as diabetes, joint issues, and
hypertension, and female users, whose data biases are propagated or even
amplified by learning models, while intersectional biases can also be observed