921 research outputs found
Validating an Air Traffic Management Concept of Operation Using Statistical Modeling
Validating a concept of operation for a complex, safety-critical system (like the National Airspace System) is challenging because of the high dimensionality of the controllable parameters and the infinite number of states of the system. In this paper, we use statistical modeling techniques to explore the behavior of a conflict detection and resolution algorithm designed for the terminal airspace. These techniques predict the robustness of the system simulation to both nominal and off-nominal behaviors within the overall airspace. They also can be used to evaluate the output of the simulation against recorded airspace data. Additionally, the techniques carry with them a mathematical value of the worth of each prediction-a statistical uncertainty for any robustness estimate. Uncertainty Quantification (UQ) is the process of quantitative characterization and ultimately a reduction of uncertainties in complex systems. UQ is important for understanding the influence of uncertainties on the behavior of a system and therefore is valuable for design, analysis, and verification and validation. In this paper, we apply advanced statistical modeling methodologies and techniques on an advanced air traffic management system, namely the Terminal Tactical Separation Assured Flight Environment (T-TSAFE). We show initial results for a parameter analysis and safety boundary (envelope) detection in the high-dimensional parameter space. For our boundary analysis, we developed a new sequential approach based upon the design of computer experiments, allowing us to incorporate knowledge from domain experts into our modeling and to determine the most likely boundary shapes and its parameters. We carried out the analysis on system parameters and describe an initial approach that will allow us to include time-series inputs, such as the radar track data, into the analysi
Generating Adversarial Examples with Adversarial Networks
Deep neural networks (DNNs) have been found to be vulnerable to adversarial
examples resulting from adding small-magnitude perturbations to inputs. Such
adversarial examples can mislead DNNs to produce adversary-selected results.
Different attack strategies have been proposed to generate adversarial
examples, but how to produce them with high perceptual quality and more
efficiently requires more research efforts. In this paper, we propose AdvGAN to
generate adversarial examples with generative adversarial networks (GANs),
which can learn and approximate the distribution of original instances. For
AdvGAN, once the generator is trained, it can generate adversarial
perturbations efficiently for any instance, so as to potentially accelerate
adversarial training as defenses. We apply AdvGAN in both semi-whitebox and
black-box attack settings. In semi-whitebox attacks, there is no need to access
the original target model after the generator is trained, in contrast to
traditional white-box attacks. In black-box attacks, we dynamically train a
distilled model for the black-box model and optimize the generator accordingly.
Adversarial examples generated by AdvGAN on different target models have high
attack success rate under state-of-the-art defenses compared to other attacks.
Our attack has placed the first with 92.76% accuracy on a public MNIST
black-box attack challenge.Comment: Accepted to IJCAI201
Bayesian Statistics and Uncertainty Quantification for Safety Boundary Analysis in Complex Systems
The analysis of a safety-critical system often requires detailed knowledge of safe regions and their highdimensional non-linear boundaries. We present a statistical approach to iteratively detect and characterize the boundaries, which are provided as parameterized shape candidates. Using methods from uncertainty quantification and active learning, we incrementally construct a statistical model from only few simulation runs and obtain statistically sound estimates of the shape parameters for safety boundaries
Effective and Efficient Federated Tree Learning on Hybrid Data
Federated learning has emerged as a promising distributed learning paradigm
that facilitates collaborative learning among multiple parties without
transferring raw data. However, most existing federated learning studies focus
on either horizontal or vertical data settings, where the data of different
parties are assumed to be from the same feature or sample space. In practice, a
common scenario is the hybrid data setting, where data from different parties
may differ both in the features and samples. To address this, we propose
HybridTree, a novel federated learning approach that enables federated tree
learning on hybrid data. We observe the existence of consistent split rules in
trees. With the help of these split rules, we theoretically show that the
knowledge of parties can be incorporated into the lower layers of a tree. Based
on our theoretical analysis, we propose a layer-level solution that does not
need frequent communication traffic to train a tree. Our experiments
demonstrate that HybridTree can achieve comparable accuracy to the centralized
setting with low computational and communication overhead. HybridTree can
achieve up to 8 times speedup compared with the other baselines
Ekiden: A Platform for Confidentiality-Preserving, Trustworthy, and Performant Smart Contract Execution
Smart contracts are applications that execute on blockchains. Today they
manage billions of dollars in value and motivate visionary plans for pervasive
blockchain deployment. While smart contracts inherit the availability and other
security assurances of blockchains, however, they are impeded by blockchains'
lack of confidentiality and poor performance.
We present Ekiden, a system that addresses these critical gaps by combining
blockchains with Trusted Execution Environments (TEEs). Ekiden leverages a
novel architecture that separates consensus from execution, enabling efficient
TEE-backed confidentiality-preserving smart-contracts and high scalability. Our
prototype (with Tendermint as the consensus layer) achieves example performance
of 600x more throughput and 400x less latency at 1000x less cost than the
Ethereum mainnet.
Another contribution of this paper is that we systematically identify and
treat the pitfalls arising from harmonizing TEEs and blockchains. Treated
separately, both TEEs and blockchains provide powerful guarantees, but
hybridized, though, they engender new attacks. For example, in naive designs,
privacy in TEE-backed contracts can be jeopardized by forgery of blocks, a
seemingly unrelated attack vector. We believe the insights learned from Ekiden
will prove to be of broad importance in hybridized TEE-blockchain systems
College Students, Networked Knowledge Activities, and Digital Competence
Amid the landscape of digital literacies and frameworks is a common assumption that contemporary youth, frequently dubbed “digital natives,” intuitively understand and use online technologies. While their use of these technologies may be frequent and highly skilled in some respects (e.g., communicating with friends), their use and abilities in other areas, such as those valued in school settings and the workforce, may differ. This survey of 350 college students examines how they use an array of online platforms for everyday life information-seeking purposes, including the frequency with which they engage in different networked knowledge activities. Findings show that while students often use platforms associated with personal networking, such as Instagram, professional platforms like LinkedIn are less commonly used. Students are much more likely to engage in passive online activities than active ones. In particular, skills related to tagging, writing, and creation are infrequently used. Additionally, about half of these college students do not believe social media, which fosters these networked knowledge activities, is relevant to their careers. These findings show opportunities for better developing college students’ digital skill sets, with guidance for skills that might be targeted, taught together, and supported through learning activities in online spaces to prepare college students for digital information tasks in the workplace
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