22,455 research outputs found
PADS: Practical Attestation for Highly Dynamic Swarm Topologies
Remote attestation protocols are widely used to detect device configuration
(e.g., software and/or data) compromise in Internet of Things (IoT) scenarios.
Unfortunately, the performances of such protocols are unsatisfactory when
dealing with thousands of smart devices. Recently, researchers are focusing on
addressing this limitation. The approach is to run attestation in a collective
way, with the goal of reducing computation and communication. Despite these
advances, current solutions for attestation are still unsatisfactory because of
their complex management and strict assumptions concerning the topology (e.g.,
being time invariant or maintaining a fixed topology). In this paper, we
propose PADS, a secure, efficient, and practical protocol for attesting
potentially large networks of smart devices with unstructured or dynamic
topologies. PADS builds upon the recent concept of non-interactive attestation,
by reducing the collective attestation problem into a minimum consensus one. We
compare PADS with a state-of-the art collective attestation protocol and
validate it by using realistic simulations that show practicality and
efficiency. The results confirm the suitability of PADS for low-end devices,
and highly unstructured networks.Comment: Submitted to ESORICS 201
Learning Deployable Navigation Policies at Kilometer Scale from a Single Traversal
Model-free reinforcement learning has recently been shown to be effective at
learning navigation policies from complex image input. However, these
algorithms tend to require large amounts of interaction with the environment,
which can be prohibitively costly to obtain on robots in the real world. We
present an approach for efficiently learning goal-directed navigation policies
on a mobile robot, from only a single coverage traversal of recorded data. The
navigation agent learns an effective policy over a diverse action space in a
large heterogeneous environment consisting of more than 2km of travel, through
buildings and outdoor regions that collectively exhibit large variations in
visual appearance, self-similarity, and connectivity. We compare pretrained
visual encoders that enable precomputation of visual embeddings to achieve a
throughput of tens of thousands of transitions per second at training time on a
commodity desktop computer, allowing agents to learn from millions of
trajectories of experience in a matter of hours. We propose multiple forms of
computationally efficient stochastic augmentation to enable the learned policy
to generalise beyond these precomputed embeddings, and demonstrate successful
deployment of the learned policy on the real robot without fine tuning, despite
environmental appearance differences at test time. The dataset and code
required to reproduce these results and apply the technique to other datasets
and robots is made publicly available at rl-navigation.github.io/deployable
GUI inspection from source code analysis
Graphical user interfaces (GUIs) are critical components of todays software. Given their increased relevance, correctness and usability of GUIs are becoming essential. This paper describes the latest results in the development of our tool to reverse engineer the GUI layer of interactive computing systems. We use static analysis techniques to generate models of the user interface behaviour from source code. Models help in graphical user interface inspection by allowing designers to concentrate on its more important aspects. One particularly type of model that the tool is able to generate is state machines. The paper shows how graph theory can be useful when applied to these models. A number of metrics and algorithms are used in the analysis of aspects of the user interface's quality. The ultimate goal of the tool is to enable analysis of interactive system through GUIs source code inspection.Fundação para a Ciência e a Tecnologia(PTDC/EIA-CCO/108995/2008
Development and Uses of Upper-division Conceptual Assessment
The use of validated conceptual assessments alongside more standard course
exams has become standard practice for the introductory courses in many physics
departments. These assessments provide a more standard measure of certain
learning goals, allowing for comparisons of student learning across
instructors, semesters, and institutions. Researchers at the University of
Colorado Boulder have developed several similar assessments designed to target
the more advanced physics content of upper-division classical mechanics,
electrostatics, quantum mechanics, and electrodynamics. Here, we synthesize the
existing research on our upper-division assessments and discuss some of the
barriers and challenges associated with developing, validating, and
implementing these assessments as well as some of the strategies we have used
to overcome these barriers.Comment: 12 pages, 5 figures, submitted to the Phys. Rev. ST - PER Focused
collection on Upper-division PE
Synthesizing Program Input Grammars
We present an algorithm for synthesizing a context-free grammar encoding the
language of valid program inputs from a set of input examples and blackbox
access to the program. Our algorithm addresses shortcomings of existing grammar
inference algorithms, which both severely overgeneralize and are prohibitively
slow. Our implementation, GLADE, leverages the grammar synthesized by our
algorithm to fuzz test programs with structured inputs. We show that GLADE
substantially increases the incremental coverage on valid inputs compared to
two baseline fuzzers
Passport: Enabling Accurate Country-Level Router Geolocation using Inaccurate Sources
When does Internet traffic cross international borders? This question has
major geopolitical, legal and social implications and is surprisingly difficult
to answer. A critical stumbling block is a dearth of tools that accurately map
routers traversed by Internet traffic to the countries in which they are
located. This paper presents Passport: a new approach for efficient, accurate
country-level router geolocation and a system that implements it. Passport
provides location predictions with limited active measurements, using machine
learning to combine information from IP geolocation databases, router
hostnames, whois records, and ping measurements. We show that Passport
substantially outperforms existing techniques, and identify cases where paths
traverse countries with implications for security, privacy, and performance
Passport: enabling accurate country-level router geolocation using inaccurate sources
When does Internet traffic cross international borders? This question has major geopolitical, legal and social implications and is surprisingly difficult to answer. A critical stumbling block is a dearth of tools that accurately map routers traversed by Internet traffic to the countries in which they are located. This paper presents Passport: a new approach for efficient, accurate country-level router geolocation and a system that implements it. Passport provides location predictions with limited active measurements, using machine learning to combine information from IP geolocation databases, router hostnames, whois records, and ping measurements. We show that Passport substantially outperforms existing techniques, and identify cases where paths traverse countries with implications for security, privacy, and performance.First author draf
Multi-criteria Evolution of Neural Network Topologies: Balancing Experience and Performance in Autonomous Systems
Majority of Artificial Neural Network (ANN) implementations in autonomous
systems use a fixed/user-prescribed network topology, leading to sub-optimal
performance and low portability. The existing neuro-evolution of augmenting
topology or NEAT paradigm offers a powerful alternative by allowing the network
topology and the connection weights to be simultaneously optimized through an
evolutionary process. However, most NEAT implementations allow the
consideration of only a single objective. There also persists the question of
how to tractably introduce topological diversification that mitigates
overfitting to training scenarios. To address these gaps, this paper develops a
multi-objective neuro-evolution algorithm. While adopting the basic elements of
NEAT, important modifications are made to the selection, speciation, and
mutation processes. With the backdrop of small-robot path-planning
applications, an experience-gain criterion is derived to encapsulate the amount
of diverse local environment encountered by the system. This criterion
facilitates the evolution of genes that support exploration, thereby seeking to
generalize from a smaller set of mission scenarios than possible with
performance maximization alone. The effectiveness of the single-objective
(optimizing performance) and the multi-objective (optimizing performance and
experience-gain) neuro-evolution approaches are evaluated on two different
small-robot cases, with ANNs obtained by the multi-objective optimization
observed to provide superior performance in unseen scenarios
- …