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Howard Davidson Arlington MA writes about how Paris Hilton washes cars & eats burgers for Carl’s Jr.
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Howard Davidson writes about about advertising, social media and marketing with a touch of sarcasm.
Howard Davidson Arlington MA -- Paris Hilton washes cars & eats burgers for ...Howard Davidson
Howard Davidson Arlington MA writes about how Paris Hilton washes cars & eats burgers for Carl’s Jr.
Howard Davidson from Arlington Massachusetts is marketing guy who helps brands connect with target audiences.
howarddavidsonarlingtonma.com
Howard Davidson writes about about advertising, social media and marketing with a touch of sarcasm.
We propose a new way of analyzing pupil measurements made in conjunction with eye tracking: fixation-aligned pupillary response averaging, in which short windows of continuous pupil measurements are selected based on patterns in eye tracking data, temporally aligned, and averaged together. Such short pupil data epochs can be selected based on fixations on a particular spot or a scan path. The windows of pupil data thus selected are aligned by temporal translation and linear warping to place corresponding parts of the gaze patterns at corresponding times and then averaged together. This approach enables the measurement of quick changes in cognitive load during visual tasks, in which task components occur at unpredictable times but are identiable via gaze data. We illustrate the method through example analyses of visual search and map reading. We conclude with a discussion of the scope and limitations of this new method.
Kinnunen Towards Task Independent Person Authentication Using Eye Movement Si...Kalle
We propose a person authentication system using eye movement signals. In security scenarios, eye-tracking has earlier been used for gaze-based password entry. A few authors have also used physical features of eye movement signals for authentication in a taskdependent
scenario with matched training and test samples. We propose and implement a task-independent scenario whereby the training and test samples can be arbitrary. We use short-term eye gaze direction to construct feature vectors which are modeled using Gaussian mixtures. The results suggest that there are personspecific features in the eye movements that can be modeled in a task-independent manner. The range of possible applications extends
beyond the security-type of authentication to proactive and user-convenience systems.
Hardoon Image Ranking With Implicit Feedback From Eye MovementsKalle
In order to help users navigate an image search system, one could
provide explicit information on a small set of images as to which
of them are relevant or not to their task. These rankings are learned
in order to present a user with a new set of images that are relevant
to their task. Requiring such explicit information may not
be feasible in a number of cases, we consider the setting where
the user provides implicit feedback, eye movements, to assist when
performing such a task. This paper explores the idea of implicitly
incorporating eye movement features in an image ranking task
where only images are available during testing. Previous work had
demonstrated that combining eye movement and image features improved
on the retrieval accuracy when compared to using each of
the sources independently. Despite these encouraging results the
proposed approach is unrealistic as no eye movements will be presented
a-priori for new images (i.e. only after the ranked images are
presented would one be able to measure a user’s eye movements
on them). We propose a novel search methodology which combines
image features together with implicit feedback from users’
eye movements in a tensor ranking Support Vector Machine and
show that it is possible to extract the individual source-specific
weight vectors. Furthermore, we demonstrate that the decomposed
image weight vector is able to construct a new image-based semantic
space that outperforms the retrieval accuracy than when solely
using the image-features.
We propose a new way of analyzing pupil measurements made in conjunction with eye tracking: fixation-aligned pupillary response averaging, in which short windows of continuous pupil measurements are selected based on patterns in eye tracking data, temporally aligned, and averaged together. Such short pupil data epochs can be selected based on fixations on a particular spot or a scan path. The windows of pupil data thus selected are aligned by temporal translation and linear warping to place corresponding parts of the gaze patterns at corresponding times and then averaged together. This approach enables the measurement of quick changes in cognitive load during visual tasks, in which task components occur at unpredictable times but are identiable via gaze data. We illustrate the method through example analyses of visual search and map reading. We conclude with a discussion of the scope and limitations of this new method.
Kinnunen Towards Task Independent Person Authentication Using Eye Movement Si...Kalle
We propose a person authentication system using eye movement signals. In security scenarios, eye-tracking has earlier been used for gaze-based password entry. A few authors have also used physical features of eye movement signals for authentication in a taskdependent
scenario with matched training and test samples. We propose and implement a task-independent scenario whereby the training and test samples can be arbitrary. We use short-term eye gaze direction to construct feature vectors which are modeled using Gaussian mixtures. The results suggest that there are personspecific features in the eye movements that can be modeled in a task-independent manner. The range of possible applications extends
beyond the security-type of authentication to proactive and user-convenience systems.
Hardoon Image Ranking With Implicit Feedback From Eye MovementsKalle
In order to help users navigate an image search system, one could
provide explicit information on a small set of images as to which
of them are relevant or not to their task. These rankings are learned
in order to present a user with a new set of images that are relevant
to their task. Requiring such explicit information may not
be feasible in a number of cases, we consider the setting where
the user provides implicit feedback, eye movements, to assist when
performing such a task. This paper explores the idea of implicitly
incorporating eye movement features in an image ranking task
where only images are available during testing. Previous work had
demonstrated that combining eye movement and image features improved
on the retrieval accuracy when compared to using each of
the sources independently. Despite these encouraging results the
proposed approach is unrealistic as no eye movements will be presented
a-priori for new images (i.e. only after the ranked images are
presented would one be able to measure a user’s eye movements
on them). We propose a novel search methodology which combines
image features together with implicit feedback from users’
eye movements in a tensor ranking Support Vector Machine and
show that it is possible to extract the individual source-specific
weight vectors. Furthermore, we demonstrate that the decomposed
image weight vector is able to construct a new image-based semantic
space that outperforms the retrieval accuracy than when solely
using the image-features.
10. Mortification
“Today it is with deep
regret and
embarrassment that I
stand before you on
behalf of my company,
11.
12. Mortification
“First off, we are sorry to
the websites that received
legal requests to remove
the pictures”
“To Filippa Hamilton, we are
sorry. You have represented
our company in several
campaigns and we hold you to
the highest esteem.”
“Above all, we are sorry to
women everywhere.”
13.
14. Bolstering
“For over 40 years, we
have worked hard to
build a brand that
represents our
customers, our country,
and ourselves. Ralph
Lauren is synonymous
with American living and
lifestyle.”
15.
16. Bolstering
“We have a moral
obligation to people
everywhere to advertise
our clothes without
distorting the true
beauty of a person.”
17.
18. Shifting Blame
“The truth is, retouching
photos is nothing new in the
fashion industry. Nearly every
single picture you see on a
billboard, magazine, on the
Internet and in stores has been
digitally altered in some way.”
“...we get caught up in the
superficial nature of our
business.”
19.
20. Corrective
action
• You Are Beautiful
organization
•Ralph Lauren will no
longer Photoshop
•sponsoring the 12th
annual Love Your Body
Day