39,116 research outputs found
Vision-based analysis of pedestrian traffic data
Reducing traffic congestion has become a major issue within urban environments. Traditional approaches, such as increasing road sizes, may prove impossible in certain scenarios, such as city centres, or ineffectual if current predictions of large growth in world traffic volumes hold true. An alternative approach lies with increasing the management efficiency of pre-existing infrastructure and public transport systems through the use of Intelligent Transportation Systems (ITS). In this paper, we focus on the requirement of obtaining robust pedestrian traffic flow data within these areas. We propose the use of a flexible and robust stereo-vision pedestrian detection and tracking approach as a basis for obtaining this information. Given this framework, we propose the use of a pedestrian indexing scheme and a suite of tools, which facilitates the declaration of user-defined pedestrian events or requests for specific statistical traffic flow data. The detection of the required events or the constant flow of statistical information can be incorporated into a variety of ITS solutions for applications in traffic management, public transport systems and urban planning
On the Relation Between Mobile Encounters and Web Traffic Patterns: A Data-driven Study
Mobility and network traffic have been traditionally studied separately.
Their interaction is vital for generations of future mobile services and
effective caching, but has not been studied in depth with real-world big data.
In this paper, we characterize mobility encounters and study the correlation
between encounters and web traffic profiles using large-scale datasets (30TB in
size) of WiFi and NetFlow traces. The analysis quantifies these correlations
for the first time, across spatio-temporal dimensions, for device types grouped
into on-the-go Flutes and sit-to-use Cellos. The results consistently show a
clear relation between mobility encounters and traffic across different
buildings over multiple days, with encountered pairs showing higher traffic
similarity than non-encountered pairs, and long encounters being associated
with the highest similarity. We also investigate the feasibility of learning
encounters through web traffic profiles, with implications for dissemination
protocols, and contact tracing. This provides a compelling case to integrate
both mobility and web traffic dimensions in future models, not only at an
individual level, but also at pairwise and collective levels. We have released
samples of code and data used in this study on GitHub, to support
reproducibility and encourage further research
(https://github.com/BabakAp/encounter-traffic).Comment: Technical report with details for conference paper at MSWiM 2018, v3
adds GitHub lin
Systemization of Pluggable Transports for Censorship Resistance
An increasing number of countries implement Internet censorship at different
scales and for a variety of reasons. In particular, the link between the
censored client and entry point to the uncensored network is a frequent target
of censorship due to the ease with which a nation-state censor can control it.
A number of censorship resistance systems have been developed thus far to help
circumvent blocking on this link, which we refer to as link circumvention
systems (LCs). The variety and profusion of attack vectors available to a
censor has led to an arms race, leading to a dramatic speed of evolution of
LCs. Despite their inherent complexity and the breadth of work in this area,
there is no systematic way to evaluate link circumvention systems and compare
them against each other. In this paper, we (i) sketch an attack model to
comprehensively explore a censor's capabilities, (ii) present an abstract model
of a LC, a system that helps a censored client communicate with a server over
the Internet while resisting censorship, (iii) describe an evaluation stack
that underscores a layered approach to evaluate LCs, and (iv) systemize and
evaluate existing censorship resistance systems that provide link
circumvention. We highlight open challenges in the evaluation and development
of LCs and discuss possible mitigations.Comment: Content from this paper was published in Proceedings on Privacy
Enhancing Technologies (PoPETS), Volume 2016, Issue 4 (July 2016) as "SoK:
Making Sense of Censorship Resistance Systems" by Sheharbano Khattak, Tariq
Elahi, Laurent Simon, Colleen M. Swanson, Steven J. Murdoch and Ian Goldberg
(DOI 10.1515/popets-2016-0028
Scalable Semantic Matching of Queries to Ads in Sponsored Search Advertising
Sponsored search represents a major source of revenue for web search engines.
This popular advertising model brings a unique possibility for advertisers to
target users' immediate intent communicated through a search query, usually by
displaying their ads alongside organic search results for queries deemed
relevant to their products or services. However, due to a large number of
unique queries it is challenging for advertisers to identify all such relevant
queries. For this reason search engines often provide a service of advanced
matching, which automatically finds additional relevant queries for advertisers
to bid on. We present a novel advanced matching approach based on the idea of
semantic embeddings of queries and ads. The embeddings were learned using a
large data set of user search sessions, consisting of search queries, clicked
ads and search links, while utilizing contextual information such as dwell time
and skipped ads. To address the large-scale nature of our problem, both in
terms of data and vocabulary size, we propose a novel distributed algorithm for
training of the embeddings. Finally, we present an approach for overcoming a
cold-start problem associated with new ads and queries. We report results of
editorial evaluation and online tests on actual search traffic. The results
show that our approach significantly outperforms baselines in terms of
relevance, coverage, and incremental revenue. Lastly, we open-source learned
query embeddings to be used by researchers in computational advertising and
related fields.Comment: 10 pages, 4 figures, 39th International ACM SIGIR Conference on
Research and Development in Information Retrieval, SIGIR 2016, Pisa, Ital
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