7,295 research outputs found
The Visual Social Distancing Problem
One of the main and most effective measures to contain the recent viral
outbreak is the maintenance of the so-called Social Distancing (SD). To comply
with this constraint, workplaces, public institutions, transports and schools
will likely adopt restrictions over the minimum inter-personal distance between
people. Given this actual scenario, it is crucial to massively measure the
compliance to such physical constraint in our life, in order to figure out the
reasons of the possible breaks of such distance limitations, and understand if
this implies a possible threat given the scene context. All of this, complying
with privacy policies and making the measurement acceptable. To this end, we
introduce the Visual Social Distancing (VSD) problem, defined as the automatic
estimation of the inter-personal distance from an image, and the
characterization of the related people aggregations. VSD is pivotal for a
non-invasive analysis to whether people comply with the SD restriction, and to
provide statistics about the level of safety of specific areas whenever this
constraint is violated. We then discuss how VSD relates with previous
literature in Social Signal Processing and indicate which existing Computer
Vision methods can be used to manage such problem. We conclude with future
challenges related to the effectiveness of VSD systems, ethical implications
and future application scenarios.Comment: 9 pages, 5 figures. All the authors equally contributed to this
manuscript and they are listed by alphabetical order. Under submissio
Inferring Unusual Crowd Events From Mobile Phone Call Detail Records
The pervasiveness and availability of mobile phone data offer the opportunity
of discovering usable knowledge about crowd behaviors in urban environments.
Cities can leverage such knowledge in order to provide better services (e.g.,
public transport planning, optimized resource allocation) and safer cities.
Call Detail Record (CDR) data represents a practical data source to detect and
monitor unusual events considering the high level of mobile phone penetration,
compared with GPS equipped and open devices. In this paper, we provide a
methodology that is able to detect unusual events from CDR data that typically
has low accuracy in terms of space and time resolution. Moreover, we introduce
a concept of unusual event that involves a large amount of people who expose an
unusual mobility behavior. Our careful consideration of the issues that come
from coarse-grained CDR data ultimately leads to a completely general framework
that can detect unusual crowd events from CDR data effectively and efficiently.
Through extensive experiments on real-world CDR data for a large city in
Africa, we demonstrate that our method can detect unusual events with 16%
higher recall and over 10 times higher precision, compared to state-of-the-art
methods. We implement a visual analytics prototype system to help end users
analyze detected unusual crowd events to best suit different application
scenarios. To the best of our knowledge, this is the first work on the
detection of unusual events from CDR data with considerations of its temporal
and spatial sparseness and distinction between user unusual activities and
daily routines.Comment: 18 pages, 6 figure
Fundamental structures of dynamic social networks
Social systems are in a constant state of flux with dynamics spanning from
minute-by-minute changes to patterns present on the timescale of years.
Accurate models of social dynamics are important for understanding spreading of
influence or diseases, formation of friendships, and the productivity of teams.
While there has been much progress on understanding complex networks over the
past decade, little is known about the regularities governing the
micro-dynamics of social networks. Here we explore the dynamic social network
of a densely-connected population of approximately 1000 individuals and their
interactions in the network of real-world person-to-person proximity measured
via Bluetooth, as well as their telecommunication networks, online social media
contacts, geo-location, and demographic data. These high-resolution data allow
us to observe social groups directly, rendering community detection
unnecessary. Starting from 5-minute time slices we uncover dynamic social
structures expressed on multiple timescales. On the hourly timescale, we find
that gatherings are fluid, with members coming and going, but organized via a
stable core of individuals. Each core represents a social context. Cores
exhibit a pattern of recurring meetings across weeks and months, each with
varying degrees of regularity. Taken together, these findings provide a
powerful simplification of the social network, where cores represent
fundamental structures expressed with strong temporal and spatial regularity.
Using this framework, we explore the complex interplay between social and
geospatial behavior, documenting how the formation of cores are preceded by
coordination behavior in the communication networks, and demonstrating that
social behavior can be predicted with high precision.Comment: Main Manuscript: 16 pages, 4 figures. Supplementary Information: 39
pages, 34 figure
Optimality and robustness in multi-robot path planning with temporal logic constraints
In this paper we present a method for automatically generating optimal robot paths satisfying high-level mission specifications. The motion of the robot in the environment is modeled as a weighted transition system. The mission is specified by an arbitrary linear temporal-logic (LTL) formula over propositions satisfied at the regions of a partitioned environment. The mission specification contains an optimizing proposition, which must be repeatedly satisfied. The cost function that we seek to minimize is the maximum time between satisfying instances of the optimizing proposition. For every environment model, and for every formula, our method computes a robot path that minimizes the cost function. The problem is motivated by applications in robotic monitoring and data-gathering. In this setting, the optimizing proposition is satisfied at all locations where data can be uploaded, and the LTL formula specifies a complex data-collection mission. Our method utilizes BĂĽchi automata to produce an automaton (which can be thought of as a graph) whose runs satisfy the temporal-logic specification. We then present a graph algorithm that computes a run corresponding to the optimal robot path. We present an implementation for a robot performing data collection in a road-network platform.This work was supported in part by the Office of Naval Research (grant number MURI N00014-09-1051), Army Research Office (grant number W911NF-09-1-0088), Air Force Office of Scientific Research (grant number YIP FA9550-09-1-020), National Science Foundation (grant number CNS-0834260), Singapore-MIT Alliance for Research and Technology (SMART) Future of Urban Mobility Project and by Natural Sciences and Engineering Research Council of Canada. (MURI N00014-09-1051 - Office of Naval Research; W911NF-09-1-0088 - Army Research Office; YIP FA9550-09-1-020 - Air Force Office of Scientific Research; CNS-0834260 - National Science Foundation; Singapore-MIT Alliance for Research and Technology (SMART); Natural Sciences and Engineering Research Council of Canada
On the Dynamics of Human Proximity for Data Diffusion in Ad-Hoc Networks
We report on a data-driven investigation aimed at understanding the dynamics
of message spreading in a real-world dynamical network of human proximity. We
use data collected by means of a proximity-sensing network of wearable sensors
that we deployed at three different social gatherings, simultaneously involving
several hundred individuals. We simulate a message spreading process over the
recorded proximity network, focusing on both the topological and the temporal
properties. We show that by using an appropriate technique to deal with the
temporal heterogeneity of proximity events, a universal statistical pattern
emerges for the delivery times of messages, robust across all the data sets.
Our results are useful to set constraints for generic processes of data
dissemination, as well as to validate established models of human mobility and
proximity that are frequently used to simulate realistic behaviors.Comment: A. Panisson et al., On the dynamics of human proximity for data
diffusion in ad-hoc networks, Ad Hoc Netw. (2011
The roundtable: an abstract model of conversation dynamics
Is it possible to abstract a formal mechanism originating schisms and
governing the size evolution of social conversations? In this work a
constructive solution to such problem is proposed: an abstract model of a
generic N-party turn-taking conversation. The model develops from simple yet
realistic assumptions derived from experimental evidence, abstracts from
conversation content and semantics while including topological information, and
is driven by stochastic dynamics. We find that a single mechanism - namely the
dynamics of conversational party's individual fitness, as related to
conversation size - controls the development of the self-organized schisming
phenomenon. Potential generalizations of the model - including individual
traits and preferences, memory effects and more elaborated conversational
topologies - may find important applications also in other fields of research,
where dynamically-interacting and networked agents play a fundamental role.Comment: 18 pages, 4 figures, to be published in Journal of Artificial
Societies and Social Simulatio
Fast and Robust Archetypal Analysis for Representation Learning
We revisit a pioneer unsupervised learning technique called archetypal
analysis, which is related to successful data analysis methods such as sparse
coding and non-negative matrix factorization. Since it was proposed, archetypal
analysis did not gain a lot of popularity even though it produces more
interpretable models than other alternatives. Because no efficient
implementation has ever been made publicly available, its application to
important scientific problems may have been severely limited. Our goal is to
bring back into favour archetypal analysis. We propose a fast optimization
scheme using an active-set strategy, and provide an efficient open-source
implementation interfaced with Matlab, R, and Python. Then, we demonstrate the
usefulness of archetypal analysis for computer vision tasks, such as codebook
learning, signal classification, and large image collection visualization
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