158 research outputs found
Fighting Online Click-Fraud Using Bluff Ads
Online advertising is currently the greatest source of revenue for many
Internet giants. The increased number of specialized websites and modern
profiling techniques, have all contributed to an explosion of the income of ad
brokers from online advertising. The single biggest threat to this growth, is
however, click-fraud. Trained botnets and even individuals are hired by
click-fraud specialists in order to maximize the revenue of certain users from
the ads they publish on their websites, or to launch an attack between
competing businesses.
In this note we wish to raise the awareness of the networking research
community on potential research areas within this emerging field. As an example
strategy, we present Bluff ads; a class of ads that join forces in order to
increase the effort level for click-fraud spammers. Bluff ads are either
targeted ads, with irrelevant display text, or highly relevant display text,
with irrelevant targeting information. They act as a litmus test for the
legitimacy of the individual clicking on the ads. Together with standard
threshold-based methods, fake ads help to decrease click-fraud levels.Comment: Draf
The Distant Heart: Mediating Long-Distance Relationships through Connected Computational Jewelry
In the world where increasingly mobility and long-distance relationships with
family, friends and loved-ones became commonplace, there exists a gap in
intimate interpersonal communication mediated by technology. Considering the
advances in the field of mediation of relationships through technology, as well
as prevalence of use of jewelry as love-tokens for expressing a wish to be
remembered and to evoke the presence of the loved-one, developments in the new
field of computational jewelry offer some truly exciting possibilities. In this
paper we investigate the role that the jewelry-like form factor of prototypes
can play in the context of studying effects of computational jewelry in
mediating long-distance relationships
Anatomy of the Third-Party Web Tracking Ecosystem
The presence of third-party tracking on websites has become customary.
However, our understanding of the third-party ecosystem is still very
rudimentary. We examine third-party trackers from a geographical perspective,
observing the third-party tracking ecosystem from 29 countries across the
globe. When examining the data by region (North America, South America, Europe,
East Asia, Middle East, and Oceania), we observe significant geographical
variation between regions and countries within regions. We find trackers that
focus on specific regions and countries, and some that are hosted in countries
outside their expected target tracking domain. Given the differences in
regulatory regimes between jurisdictions, we believe this analysis sheds light
on the geographical properties of this ecosystem and on the problems that these
may pose to our ability to track and manage the different data silos that now
store personal data about us all
Wearable Computing for Health and Fitness: Exploring the Relationship between Data and Human Behaviour
Health and fitness wearable technology has recently advanced, making it
easier for an individual to monitor their behaviours. Previously self generated
data interacts with the user to motivate positive behaviour change, but issues
arise when relating this to long term mention of wearable devices. Previous
studies within this area are discussed. We also consider a new approach where
data is used to support instead of motivate, through monitoring and logging to
encourage reflection. Based on issues highlighted, we then make recommendations
on the direction in which future work could be most beneficial
Towards Machine Learning and Inference for Resource-constrained MCUs
Machine learning (ML) is moving towards edge devices. However, ML models with
high computational demands and energy consumption pose challenges for ML
inference in resource-constrained environments, such as the deep sea. To
address these challenges, we propose a battery-free ML inference and model
personalization pipeline for microcontroller units (MCUs). As an example, we
performed fish image recognition in the ocean. We evaluated and compared the
accuracy, runtime, power, and energy consumption of the model before and after
optimization. The results demonstrate that, our pipeline can achieve 97.78%
accuracy with 483.82 KB Flash, 70.32 KB RAM, 118 ms runtime, 4.83 mW power, and
0.57 mJ energy consumption on MCUs, reducing by 64.17%, 12.31%, 52.42%, 63.74%,
and 82.67%, compared to the baseline. The results indicate the feasibility of
battery-free ML inference on MCUs.Comment: Poster accepted by the 21st ACM International Conference on Mobile
Systems, Applications, and Services (ACM MobiSys 2023
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