70,752 research outputs found
Learning the Designer's Preferences to Drive Evolution
This paper presents the Designer Preference Model, a data-driven solution
that pursues to learn from user generated data in a Quality-Diversity
Mixed-Initiative Co-Creativity (QD MI-CC) tool, with the aims of modelling the
user's design style to better assess the tool's procedurally generated content
with respect to that user's preferences. Through this approach, we aim for
increasing the user's agency over the generated content in a way that neither
stalls the user-tool reciprocal stimuli loop nor fatigues the user with
periodical suggestion handpicking. We describe the details of this novel
solution, as well as its implementation in the MI-CC tool the Evolutionary
Dungeon Designer. We present and discuss our findings out of the initial tests
carried out, spotting the open challenges for this combined line of research
that integrates MI-CC with Procedural Content Generation through Machine
Learning.Comment: 16 pages, Accepted and to appear in proceedings of the 23rd European
Conference on the Applications of Evolutionary and bio-inspired Computation,
EvoApplications 202
Personalized Cinemagraphs using Semantic Understanding and Collaborative Learning
Cinemagraphs are a compelling way to convey dynamic aspects of a scene. In
these media, dynamic and still elements are juxtaposed to create an artistic
and narrative experience. Creating a high-quality, aesthetically pleasing
cinemagraph requires isolating objects in a semantically meaningful way and
then selecting good start times and looping periods for those objects to
minimize visual artifacts (such a tearing). To achieve this, we present a new
technique that uses object recognition and semantic segmentation as part of an
optimization method to automatically create cinemagraphs from videos that are
both visually appealing and semantically meaningful. Given a scene with
multiple objects, there are many cinemagraphs one could create. Our method
evaluates these multiple candidates and presents the best one, as determined by
a model trained to predict human preferences in a collaborative way. We
demonstrate the effectiveness of our approach with multiple results and a user
study.Comment: To appear in ICCV 2017. Total 17 pages including the supplementary
materia
Study on Scheduling Techniques for Ultra Dense Small Cell Networks
The most promising approach to enhance network capacity for the next
generation of wireless cellular networks (5G) is densification, which benefits
from the extensive spatial reuse of the spectrum and the reduced distance
between transmitters and receivers. In this paper, we examine the performance
of different schedulers in ultra dense small cell deployments. Due to the
stronger line of sight (LOS) at low inter-site distances (ISDs), we discuss
that the Rician fading channel model is more suitable to study network
performance than the Rayleigh one, and model the Rician K factor as a function
of distance between the user equipment (UE) and its serving base station (BS).
We also construct a cross-correlation shadowing model that takes into account
the ISD, and finally investigate potential multi-user diversity gains in ultra
dense small cell deployments by comparing the performances of proportional fair
(PF) and round robin (RR) schedulers. Our study shows that as network becomes
denser, the LOS component starts to dominate the path loss model which
significantly increases the interference. Simulation results also show that
multi-user diversity is considerably reduced at low ISDs, and thus the PF
scheduling gain over the RR one is small, around 10% in terms of cell
throughput. As a result, the RR scheduling may be preferred for dense small
cell deployments due to its simplicity. Despite both the interference
aggravation as well as the multi-user diversity loss, network densification is
still worth it from a capacity view point.Comment: 6 pages, 7 figures, Accepted to IEEE VTC-Fall 2015 Bosto
Oral messages improve visual search
Input multimodality combining speech and hand gestures has motivated numerous
usability studies. Contrastingly, issues relating to the design and ergonomic
evaluation of multimodal output messages combining speech with visual
modalities have not yet been addressed extensively. The experimental study
presented here addresses one of these issues. Its aim is to assess the actual
efficiency and usability of oral system messages including brief spatial
information for helping users to locate objects on crowded displays rapidly.
Target presentation mode, scene spatial structure and task difficulty were
chosen as independent variables. Two conditions were defined: the visual target
presentation mode (VP condition) and the multimodal target presentation mode
(MP condition). Each participant carried out two blocks of visual search tasks
(120 tasks per block, and one block per condition). Scene target presentation
mode, scene structure and task difficulty were found to be significant factors.
Multimodal target presentation proved to be more efficient than visual target
presentation. In addition, participants expressed very positive judgments on
multimodal target presentations which were preferred to visual presentations by
a majority of participants. Besides, the contribution of spatial messages to
visual search speed and accuracy was influenced by scene spatial structure and
task difficulty: (i) messages improved search efficiency to a lesser extent for
2D array layouts than for some other symmetrical layouts, although the use of
2D arrays for displaying pictures is currently prevailing; (ii) message
usefulness increased with task difficulty. Most of these results are
statistically significant.Comment: 4 page
Genetic Programming for Smart Phone Personalisation
Personalisation in smart phones requires adaptability to dynamic context
based on user mobility, application usage and sensor inputs. Current
personalisation approaches, which rely on static logic that is developed a
priori, do not provide sufficient adaptability to dynamic and unexpected
context. This paper proposes genetic programming (GP), which can evolve program
logic in realtime, as an online learning method to deal with the highly dynamic
context in smart phone personalisation. We introduce the concept of
collaborative smart phone personalisation through the GP Island Model, in order
to exploit shared context among co-located phone users and reduce convergence
time. We implement these concepts on real smartphones to demonstrate the
capability of personalisation through GP and to explore the benefits of the
Island Model. Our empirical evaluations on two example applications confirm
that the Island Model can reduce convergence time by up to two-thirds over
standalone GP personalisation.Comment: 43 pages, 11 figure
Event Organization 101: Understanding Latent Factors of Event Popularity
The problem of understanding people's participation in real-world events has
been a subject of active research and can offer valuable insights for human
behavior analysis and event-related recommendation/advertisement. In this work,
we study the latent factors for determining event popularity using large-scale
datasets collected from the popular Meetup.com EBSN in three major cities
around the world. We have conducted modeling analysis of four contextual
factors (spatial, group, temporal, and semantic), and also developed a
group-based social influence propagation network to model group-specific
influences on events. By combining the Contextual features And Social Influence
NetwOrk, our integrated prediction framework CASINO can capture the diverse
influential factors of event participation and can be used by event organizers
to predict/improve the popularity of their events. Evaluations demonstrate that
our CASINO framework achieves high prediction accuracy with contributions from
all the latent features we capture.Comment: International AAAI Conference on Web and Social Media (ICWSM) 2017
https://www.aaai.org/ocs/index.php/ICWSM/ICWSM17/paper/view/1557
Sequences of purchases in credit card data reveal life styles in urban populations
Zipf-like distributions characterize a wide set of phenomena in physics,
biology, economics and social sciences. In human activities, Zipf-laws describe
for example the frequency of words appearance in a text or the purchases types
in shopping patterns. In the latter, the uneven distribution of transaction
types is bound with the temporal sequences of purchases of individual choices.
In this work, we define a framework using a text compression technique on the
sequences of credit card purchases to detect ubiquitous patterns of collective
behavior. Clustering the consumers by their similarity in purchases sequences,
we detect five consumer groups. Remarkably, post checking, individuals in each
group are also similar in their age, total expenditure, gender, and the
diversity of their social and mobility networks extracted by their mobile phone
records. By properly deconstructing transaction data with Zipf-like
distributions, this method uncovers sets of significant sequences that reveal
insights on collective human behavior.Comment: 30 pages, 26 figure
Landscape preferences, ecological quality and biodiversity protection
The loss of biological diversity is a major environmental problem occurring on a global scale. Human-environment researchers have an important role in shaping policy and programs at a local, national and international level. This paper explores human preference for landscapes relative to ecological quality and assesses the relationship between these preferences and land management behavior. A survey of more than 1000 urban and rural residents of southeastern Australia examined preferences for 36 black and white photographs of native vegetation. There was more commonality than difference between urban and rural preference for different arrays of native vegetation. Preference for Eucalyptus species was higher than preference for non-Eucalyptus species. Preference ratings indicate minimal differences across landscapes with distinct variation in ecological quality. The study suggests that preference for landscapes of relatively high ecological quality is associated with behavior that is protective of this resource
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