14,956 research outputs found
Towards Baselines for Shoulder Surfing on Mobile Authentication
Given the nature of mobile devices and unlock procedures, unlock
authentication is a prime target for credential leaking via shoulder surfing, a
form of an observation attack. While the research community has investigated
solutions to minimize or prevent the threat of shoulder surfing, our
understanding of how the attack performs on current systems is less well
studied. In this paper, we describe a large online experiment (n=1173) that
works towards establishing a baseline of shoulder surfing vulnerability for
current unlock authentication systems. Using controlled video recordings of a
victim entering in a set of 4- and 6-length PINs and Android unlock patterns on
different phones from different angles, we asked participants to act as
attackers, trying to determine the authentication input based on the
observation. We find that 6-digit PINs are the most elusive attacking surface
where a single observation leads to just 10.8% successful attacks, improving to
26.5\% with multiple observations. As a comparison, 6-length Android patterns,
with one observation, suffered 64.2% attack rate and 79.9% with multiple
observations. Removing feedback lines for patterns improves security from
35.3\% and 52.1\% for single and multiple observations, respectively. This
evidence, as well as other results related to hand position, phone size, and
observation angle, suggests the best and worst case scenarios related to
shoulder surfing vulnerability which can both help inform users to improve
their security choices, as well as establish baselines for researchers.Comment: Will appear in Annual Computer Security Applications Conference
(ACSAC
Constructing Datasets for Multi-hop Reading Comprehension Across Documents
Most Reading Comprehension methods limit themselves to queries which can be
answered using a single sentence, paragraph, or document. Enabling models to
combine disjoint pieces of textual evidence would extend the scope of machine
comprehension methods, but currently there exist no resources to train and test
this capability. We propose a novel task to encourage the development of models
for text understanding across multiple documents and to investigate the limits
of existing methods. In our task, a model learns to seek and combine evidence -
effectively performing multi-hop (alias multi-step) inference. We devise a
methodology to produce datasets for this task, given a collection of
query-answer pairs and thematically linked documents. Two datasets from
different domains are induced, and we identify potential pitfalls and devise
circumvention strategies. We evaluate two previously proposed competitive
models and find that one can integrate information across documents. However,
both models struggle to select relevant information, as providing documents
guaranteed to be relevant greatly improves their performance. While the models
outperform several strong baselines, their best accuracy reaches 42.9% compared
to human performance at 74.0% - leaving ample room for improvement.Comment: This paper directly corresponds to the TACL version
(https://transacl.org/ojs/index.php/tacl/article/view/1325) apart from minor
changes in wording, additional footnotes, and appendice
Water Quality Trading and Agricultural Nonpoint Source Pollution: An Analysis of the Effectiveness and Fairness of EPA's Policy on Water Quality Trading
Water quality problems continue to plague our nation, even though Congress passed the Clean Water Act (CWA) to "restore and maintain the chemical, physical, and biological integrity of the Nation's waters"1 more than three decades ago. During the past thirty years, the dominant sources of water pollution have changed, requiring us to seek new approaches for cleaning up our waters. Water quality trading has been heralded as an approach that can integrate market mechanisms into the effort of cleaning up our water. This Article examines the Environmental Protection Agency's (EPA) policy on water quality trading and the prospects for water quality trading to help improve water quality.Part II briefly describes our water quality problems and causes. Part III examines the theoretical basis for trading and the EPA's Water Quality Trading Policy. Part IV discusses the potential impact of total maximum daily loads (TMDLs) on water quality trading, and Part V analyzes potential problems that water quality trading programs confront. Part VI addresses distributional and efficiency concerns that arise when considering trading and agricultural nonpoint source pollution. Part VII then examines issues relating to water quality trading and state laws before reaching conclusions and recommendations in Part VIII
A Diagram Is Worth A Dozen Images
Diagrams are common tools for representing complex concepts, relationships
and events, often when it would be difficult to portray the same information
with natural images. Understanding natural images has been extensively studied
in computer vision, while diagram understanding has received little attention.
In this paper, we study the problem of diagram interpretation and reasoning,
the challenging task of identifying the structure of a diagram and the
semantics of its constituents and their relationships. We introduce Diagram
Parse Graphs (DPG) as our representation to model the structure of diagrams. We
define syntactic parsing of diagrams as learning to infer DPGs for diagrams and
study semantic interpretation and reasoning of diagrams in the context of
diagram question answering. We devise an LSTM-based method for syntactic
parsing of diagrams and introduce a DPG-based attention model for diagram
question answering. We compile a new dataset of diagrams with exhaustive
annotations of constituents and relationships for over 5,000 diagrams and
15,000 questions and answers. Our results show the significance of our models
for syntactic parsing and question answering in diagrams using DPGs
The TRECVID 2007 BBC rushes summarization evaluation pilot
This paper provides an overview of a pilot evaluation of
video summaries using rushes from several BBC dramatic series. It was carried out under the auspices of TRECVID.
Twenty-two research teams submitted video summaries of
up to 4% duration, of 42 individual rushes video files aimed
at compressing out redundant and insignificant material.
The output of two baseline systems built on straightforward
content reduction techniques was contributed by Carnegie
Mellon University as a control. Procedures for developing
ground truth lists of important segments from each video
were developed at Dublin City University and applied to
the BBC video. At NIST each summary was judged by
three humans with respect to how much of the ground truth
was included, how easy the summary was to understand,
and how much repeated material the summary contained.
Additional objective measures included: how long it took
the system to create the summary, how long it took the assessor to judge it against the ground truth, and what the
summary's duration was. Assessor agreement on finding desired segments averaged 78% and results indicate that while it is difficult to exceed the performance of baselines, a few systems did
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