267 research outputs found
The Importance of Category Labels in Grammar Induction with Child-directed Utterances
Recent progress in grammar induction has shown that grammar induction is
possible without explicit assumptions of language-specific knowledge. However,
evaluation of induced grammars usually has ignored phrasal labels, an essential
part of a grammar. Experiments in this work using a labeled evaluation metric,
RH, show that linguistically motivated predictions about grammar sparsity and
use of categories can only be revealed through labeled evaluation. Furthermore,
depth-bounding as an implementation of human memory constraints in grammar
inducers is still effective with labeled evaluation on multilingual transcribed
child-directed utterances.Comment: The 16th International Conference on Parsing Technologies (IWPT 2020
Fairness-aware Regression Robust to Adversarial Attacks
In this paper, we take a first step towards answering the question of how to
design fair machine learning algorithms that are robust to adversarial attacks.
Using a minimax framework, we aim to design an adversarially robust fair
regression model that achieves optimal performance in the presence of an
attacker who is able to add a carefully designed adversarial data point to the
dataset or perform a rank-one attack on the dataset. By solving the proposed
nonsmooth nonconvex-nonconcave minimax problem, the optimal adversary as well
as the robust fairness-aware regression model are obtained. For both synthetic
data and real-world datasets, numerical results illustrate that the proposed
adversarially robust fair models have better performance on poisoned datasets
than other fair machine learning models in both prediction accuracy and
group-based fairness measure
Wireless Power Transfer in Massive MIMO Aided HetNets with User Association
This paper explores the potential of wireless power transfer (WPT) in massive
multiple input multiple output (MIMO) aided heterogeneous networks (HetNets),
where massive MIMO is applied in the macrocells, and users aim to harvest as
much energy as possible and reduce the uplink path loss for enhancing their
information transfer. By addressing the impact of massive MIMO on the user
association, we compare and analyze two user association schemes. We adopt the
linear maximal ratio transmission beam-forming for massive MIMO power transfer
to recharge users. By deriving new statistical properties, we obtain the exact
and asymptotic expressions for the average harvested energy. Then we derive the
average uplink achievable rate under the harvested energy constraint.Comment: 36 pages, 11 figures, to appear in IEEE Transactions on
Communication
A comparative study of two molecular mechanics models based on harmonic potentials
We show that the two molecular mechanics models, the stick-spiral and the
beam models, predict considerably different mechanical properties of materials
based on energy equivalence. The difference between the two models is
independent of the materials since all parameters of the beam model are
obtained from the harmonic potentials. We demonstrate this difference for
finite width graphene nanoribbons and a single polyethylene chain comparing
results of the molecular dynamics (MD) simulations with harmonic potentials and
the finite element method with the beam model. We also find that the difference
strongly depends on the loading modes, chirality and width of the graphene
nanoribbons, and it increases with decreasing width of the nanoribbons under
pure bending condition. The maximum difference of the predicted mechanical
properties using the two models can exceed 300% in different loading modes.
Comparing the two models with the MD results of AIREBO potential, we find that
the stick-spiral model overestimates and the beam model underestimates the
mechanical properties in narrow armchair graphene nanoribbons under pure
bending condition.Comment: 40 pages, 21 figure
A Security, Privacy and Trust Methodology for IIoT
The implements of IoT and industrial IoT (IIoT) are increasingly becoming the consensus with Industry 4.0. Relevant data-driven methodologies are typically concentrated on the scoring systems of CVE prioritization schemes, the scoring formulas of CVSS metrics, and other vulnerability impact factors. However, these prioritized lists such as the CWE/SANS Top 25 suffer from a critical weakness: they fail to consider empirical evidence of exploits. Considering the distinct properties and specific risks of SCADA systems in IIoT, this paper overcomes the inherent limitation of IIoT empirical research which is the sample size of exploits by collecting data manually. This study then developed an exploits factors-embedded regression model to statistically access the significant relationships between security, privacy, and trust-based vulnerability attributes. Through this data-driven empirical methodology, the study elucidated the interactions of security, privacy, and trust in IIoT with professional quantitative indicators, which would provide grounds for substantial further related work. In addition to the security privacy and trust regression analysis, this study further explores the impact of IoT and IIoT by difference-in-difference (DID) approach, applying bootstrap standard error with Kernel option and quantile DID test to evaluate the robustness of DID model. In general, the empirical results indicated that: 1) the CVSS score of vulnerability is irrelevant to the disclosure of exploits, but is positively correlated with CWEs by Density and CVE year, 2) among the exploits of SCADA-related authors, the more identical CWEs that exist in these exploits, the higher the CVSS score of the exploit CVE will be, and CVE year has a negative moderating effect within this relationship; 3) the CVSS scores of SCADA exploits have significantly decreased in comparison with non-SCADA after the promulgation of Industry 4.0
A New Look at Physical Layer Security, Caching, and Wireless Energy Harvesting for Heterogeneous Ultra-dense Networks
Heterogeneous ultra-dense networks enable ultra-high data rates and ultra-low
latency through the use of dense sub-6 GHz and millimeter wave (mmWave) small
cells with different antenna configurations. Existing work has widely studied
spectral and energy efficiency in such networks and shown that high spectral
and energy efficiency can be achieved. This article investigates the benefits
of heterogeneous ultra-dense network architecture from the perspectives of
three promising technologies, i.e., physical layer security, caching, and
wireless energy harvesting, and provides enthusiastic outlook towards
application of these technologies in heterogeneous ultra-dense networks. Based
on the rationale of each technology, opportunities and challenges are
identified to advance the research in this emerging network.Comment: Accepted to appear in IEEE Communications Magazin
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