88,297 research outputs found
Towards Identifying and closing Gaps in Assurance of autonomous Road vehicleS - a collection of Technical Notes Part 1
This report provides an introduction and overview of the Technical Topic Notes (TTNs) produced in the Towards Identifying and closing Gaps in Assurance of autonomous Road vehicleS (Tigars) project. These notes aim to support the development and evaluation of autonomous vehicles. Part 1 addresses: Assurance-overview and issues, Resilience and Safety Requirements, Open Systems Perspective and Formal Verification and Static Analysis of ML Systems. Part 2: Simulation and Dynamic Testing, Defence in Depth and Diversity, Security-Informed Safety Analysis, Standards and Guidelines
Citizen Social Lab: A digital platform for human behaviour experimentation within a citizen science framework
Cooperation is one of the behavioral traits that define human beings, however
we are still trying to understand why humans cooperate. Behavioral experiments
have been largely conducted to shed light into the mechanisms behind
cooperation and other behavioral traits. However, most of these experiments
have been conducted in laboratories with highly controlled experimental
protocols but with varied limitations which limits the reproducibility and the
generalization of the results obtained. In an attempt to overcome these
limitations, some experimental approaches have moved human behavior
experimentation from laboratories to public spaces, where behaviors occur
naturally, and have opened the participation to the general public within the
citizen science framework. Given the open nature of these environments, it is
critical to establish the appropriate protocols to maintain the same data
quality that one can obtain in the laboratories. Here, we introduce Citizen
Social Lab, a software platform designed to be used in the wild using citizen
science practices. The platform allows researchers to collect data in a more
realistic context while maintaining the scientific rigour, and it is structured
in a modular and scalable way so it can also be easily adapted for online or
brick-and-mortar experimental laboratories. Following citizen science
guidelines, the platform is designed to motivate a more general population into
participation, but also to promote engaging and learning of the scientific
research process. We also review the main results of the experiments performed
using the platform up to now, and the set of games that each experiment
includes. Finally, we evaluate some properties of the platform, such as the
heterogeneity of the samples of the experiments and their satisfaction level,
and the parameters that demonstrate the robustness of the platform and the
quality of the data collected.Comment: 17 pages, 11 figures and 4 table
Robust Monotonic Optimization Framework for Multicell MISO Systems
The performance of multiuser systems is both difficult to measure fairly and
to optimize. Most resource allocation problems are non-convex and NP-hard, even
under simplifying assumptions such as perfect channel knowledge, homogeneous
channel properties among users, and simple power constraints. We establish a
general optimization framework that systematically solves these problems to
global optimality. The proposed branch-reduce-and-bound (BRB) algorithm handles
general multicell downlink systems with single-antenna users, multiantenna
transmitters, arbitrary quadratic power constraints, and robustness to channel
uncertainty. A robust fairness-profile optimization (RFO) problem is solved at
each iteration, which is a quasi-convex problem and a novel generalization of
max-min fairness. The BRB algorithm is computationally costly, but it shows
better convergence than the previously proposed outer polyblock approximation
algorithm. Our framework is suitable for computing benchmarks in general
multicell systems with or without channel uncertainty. We illustrate this by
deriving and evaluating a zero-forcing solution to the general problem.Comment: Published in IEEE Transactions on Signal Processing, 16 pages, 9
figures, 2 table
Embedded model discrepancy: A case study of Zika modeling
Mathematical models of epidemiological systems enable investigation of and
predictions about potential disease outbreaks. However, commonly used models
are often highly simplified representations of incredibly complex systems.
Because of these simplifications, the model output, of say new cases of a
disease over time, or when an epidemic will occur, may be inconsistent with
available data. In this case, we must improve the model, especially if we plan
to make decisions based on it that could affect human health and safety, but
direct improvements are often beyond our reach. In this work, we explore this
problem through a case study of the Zika outbreak in Brazil in 2016. We propose
an embedded discrepancy operator---a modification to the model equations that
requires modest information about the system and is calibrated by all relevant
data. We show that the new enriched model demonstrates greatly increased
consistency with real data. Moreover, the method is general enough to easily
apply to many other mathematical models in epidemiology.Comment: 9 pages, 7 figure
Byzantine Attack and Defense in Cognitive Radio Networks: A Survey
The Byzantine attack in cooperative spectrum sensing (CSS), also known as the
spectrum sensing data falsification (SSDF) attack in the literature, is one of
the key adversaries to the success of cognitive radio networks (CRNs). In the
past couple of years, the research on the Byzantine attack and defense
strategies has gained worldwide increasing attention. In this paper, we provide
a comprehensive survey and tutorial on the recent advances in the Byzantine
attack and defense for CSS in CRNs. Specifically, we first briefly present the
preliminaries of CSS for general readers, including signal detection
techniques, hypothesis testing, and data fusion. Second, we analyze the spear
and shield relation between Byzantine attack and defense from three aspects:
the vulnerability of CSS to attack, the obstacles in CSS to defense, and the
games between attack and defense. Then, we propose a taxonomy of the existing
Byzantine attack behaviors and elaborate on the corresponding attack
parameters, which determine where, who, how, and when to launch attacks. Next,
from the perspectives of homogeneous or heterogeneous scenarios, we classify
the existing defense algorithms, and provide an in-depth tutorial on the
state-of-the-art Byzantine defense schemes, commonly known as robust or secure
CSS in the literature. Furthermore, we highlight the unsolved research
challenges and depict the future research directions.Comment: Accepted by IEEE Communications Surveys and Tutoiral
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