164 research outputs found
Addressing Non-IID Problem in Federated Autonomous Driving with Contrastive Divergence Loss
Federated learning has been widely applied in autonomous driving since it
enables training a learning model among vehicles without sharing users' data.
However, data from autonomous vehicles usually suffer from the
non-independent-and-identically-distributed (non-IID) problem, which may cause
negative effects on the convergence of the learning process. In this paper, we
propose a new contrastive divergence loss to address the non-IID problem in
autonomous driving by reducing the impact of divergence factors from
transmitted models during the local learning process of each silo. We also
analyze the effects of contrastive divergence in various autonomous driving
scenarios, under multiple network infrastructures, and with different
centralized/distributed learning schemes. Our intensive experiments on three
datasets demonstrate that our proposed contrastive divergence loss further
improves the performance over current state-of-the-art approaches
Helix++: A platform for efficiently securing software
The open-source Helix++ project improves the security posture of computing
platforms by applying cutting-edge cybersecurity techniques to diversify and
harden software automatically. A distinguishing feature of Helix++ is that it
does not require source code or build artifacts; it operates directly on
software in binary form--even stripped executables and libraries. This feature
is key as rebuilding applications from source is a time-consuming and often
frustrating process. Diversification breaks the software monoculture and makes
attacks harder to execute as information needed for a successful attack will
have changed unpredictably. Diversification also forces attackers to customize
an attack for each target instead of attackers crafting an exploit that works
reliably on all similarly configured targets. Hardening directly targets key
attack classes. The combination of diversity and hardening provides
defense-in-depth, as well as a moving target defense, to secure the Nation's
cyber infrastructure.Comment: 4 pages, 1 figure, white pape
Automated Mobile System for Accurate Outdoor Tree Crop Enumeration Using an Uncalibrated Camera.
This paper demonstrates an automated computer vision system for outdoor tree crop enumeration in a seedling nursery. The complete system incorporates both hardware components (including an embedded microcontroller, an odometry encoder, and an uncalibrated digital color camera) and software algorithms (including microcontroller algorithms and the proposed algorithm for tree crop enumeration) required to obtain robust performance in a natural outdoor environment. The enumeration system uses a three-step image analysis process based upon: (1) an orthographic plant projection method integrating a perspective transform with automatic parameter estimation; (2) a plant counting method based on projection histograms; and (3) a double-counting avoidance method based on a homography transform. Experimental results demonstrate the ability to count large numbers of plants automatically with no human effort. Results show that, for tree seedlings having a height up to 40 cm and a within-row tree spacing of approximately 10 cm, the algorithms successfully estimated the number of plants with an average accuracy of 95.2% for trees within a single image and 98% for counting of the whole plant population in a large sequence of images
Music-Driven Group Choreography
Music-driven choreography is a challenging problem with a wide variety of
industrial applications. Recently, many methods have been proposed to
synthesize dance motions from music for a single dancer. However, generating
dance motion for a group remains an open problem. In this paper, we present
, a new large-scale dataset for music-driven group dance
generation. Unlike existing datasets that only support single dance, our new
dataset contains group dance videos, hence supporting the study of group
choreography. We propose a semi-autonomous labeling method with humans in the
loop to obtain the 3D ground truth for our dataset. The proposed dataset
consists of 16.7 hours of paired music and 3D motion from in-the-wild videos,
covering 7 dance styles and 16 music genres. We show that naively applying
single dance generation technique to creating group dance motion may lead to
unsatisfactory results, such as inconsistent movements and collisions between
dancers. Based on our new dataset, we propose a new method that takes an input
music sequence and a set of 3D positions of dancers to efficiently produce
multiple group-coherent choreographies. We propose new evaluation metrics for
measuring group dance quality and perform intensive experiments to demonstrate
the effectiveness of our method. Our project facilitates future research on
group dance generation and is available at:
https://aioz-ai.github.io/AIOZ-GDANCE/Comment: accepted in CVPR 202
Same Coverage, Less Bloat: Accelerating Binary-only Fuzzing with Coverage-preserving Coverage-guided Tracing
Coverage-guided fuzzing's aggressive, high-volume testing has helped reveal
tens of thousands of software security flaws. While executing billions of test
cases mandates fast code coverage tracing, the nature of binary-only targets
leads to reduced tracing performance. A recent advancement in binary fuzzing
performance is Coverage-guided Tracing (CGT), which brings orders-of-magnitude
gains in throughput by restricting the expense of coverage tracing to only when
new coverage is guaranteed. Unfortunately, CGT suits only a basic block
coverage granularity -- yet most fuzzers require finer-grain coverage metrics:
edge coverage and hit counts. It is this limitation which prohibits nearly all
of today's state-of-the-art fuzzers from attaining the performance benefits of
CGT.
This paper tackles the challenges of adapting CGT to fuzzing's most
ubiquitous coverage metrics. We introduce and implement a suite of enhancements
that expand CGT's introspection to fuzzing's most common code coverage metrics,
while maintaining its orders-of-magnitude speedup over conventional always-on
coverage tracing. We evaluate their trade-offs with respect to fuzzing
performance and effectiveness across 12 diverse real-world binaries (8 open-
and 4 closed-source). On average, our coverage-preserving CGT attains
near-identical speed to the present block-coverage-only CGT, UnTracer; and
outperforms leading binary- and source-level coverage tracers QEMU, Dyninst,
RetroWrite, and AFL-Clang by 2-24x, finding more bugs in less time.Comment: CCS '21: Proceedings of the 2021 ACM SIGSAC Conference on Computer
and Communications Securit
GP-SUM. Gaussian Processes Filtering of non-Gaussian Beliefs
This work studies the problem of stochastic dynamic filtering and state
propagation with complex beliefs. The main contribution is GP-SUM, a filtering
algorithm tailored to dynamic systems and observation models expressed as
Gaussian Processes (GP), and to states represented as a weighted sum of
Gaussians. The key attribute of GP-SUM is that it does not rely on
linearizations of the dynamic or observation models, or on unimodal Gaussian
approximations of the belief, hence enables tracking complex state
distributions. The algorithm can be seen as a combination of a sampling-based
filter with a probabilistic Bayes filter. On the one hand, GP-SUM operates by
sampling the state distribution and propagating each sample through the dynamic
system and observation models. On the other hand, it achieves effective
sampling and accurate probabilistic propagation by relying on the GP form of
the system, and the sum-of-Gaussian form of the belief. We show that GP-SUM
outperforms several GP-Bayes and Particle Filters on a standard benchmark. We
also demonstrate its use in a pushing task, predicting with experimental
accuracy the naturally occurring non-Gaussian distributions.Comment: WAFR 2018, 16 pages, 7 figure
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