arXiv subjects

Kevin Lee

Publications and source records attributed to Kevin Lee.

32 records · Page 2Linked to original sources

arXiv2022

Adversarial Attacks on Speech Recognition Systems for Mission-Critical Applications: A Survey

A Machine-Critical Application is a system that is fundamentally necessary to the success of specific and sensitive operations such as search and recovery, rescue, military, and emergency management actions. Recent advances in Machine Learning, Natural Language Processing, voice recognition, and speech processing technologies have naturally allowed the development and deployment of speech-based conversational interfaces to interact with various machine-critical applications. While these conversational interfaces have allowed users to give voice commands to carry out strategic and critical activities, their robustness to adversarial attacks remains uncertain and unclear. Indeed, Adversarial Artificial Intelligence (AI) which refers to a set of techniques that attempt to fool machine learning models with deceptive data, is a growing threat in the AI and machine learning research community, in particular for machine-critical applications. The most common reason of adversarial attacks is to cause a malfunction in a machine learning model. An adversarial attack might entail presenting a model with inaccurate or fabricated samples as it's training data, or introducing maliciously designed data to deceive an already trained model. While focusing on speech recognition for machine-critical applications, in this paper, we first review existing speech recognition techniques, then, we investigate the effectiveness of adversarial attacks and defenses against these systems, before outlining research challenges, defense recommendations, and future work. This paper is expected to serve researchers and practitioners as a reference to help them in understanding the challenges, position themselves and, ultimately, help them to improve existing models of speech recognition for mission-critical applications. Keywords: Mission-Critical Applications, Adversarial AI, Speech Recognition Systems.

cs.SD↗

arXiv2021

Adapting Security Warnings to Counter Online Disinformation

Disinformation is proliferating on the internet, and platforms are responding by attaching warnings to content. There is little evidence, however, that these warnings help users identify or avoid disinformation. In this work, we adapt methods and results from the information security warning literature in order to design and evaluate effective disinformation warnings. In an initial laboratory study, we used a simulated search task to examine contextual and interstitial disinformation warning designs. We found that users routinely ignore contextual warnings, but users notice interstitial warnings -- and respond by seeking information from alternative sources. We then conducted a follow-on crowdworker study with eight interstitial warning designs. We confirmed a significant impact on user information-seeking behavior, and we found that a warning's design could effectively inform users or convey a risk of harm. We also found, however, that neither user comprehension nor fear of harm moderated behavioral effects. Our work provides evidence that disinformation warnings can -- when designed well -- help users identify and avoid disinformation. We show a path forward for designing effective warnings, and we contribute repeatable methods for evaluating behavioral effects. We also surface a possible dilemma: disinformation warnings might be able to inform users and guide behavior, but the behavioral effects might result from user experience friction, not informed decision making.

cs.HC↗

arXiv2021

Dislocation and Indium Droplet Related Emission Inhomogeneities in InGaN LEDs

This report classifies emission inhomogeneities that manifest in InGaN quantum well blue light-emitting diodes grown by plasma-assisted molecular beam epitaxy on free-standing GaN substrates. By a combination of spatially resolved electroluminescence and cathodoluminescence measurements, atomic force microscopy, scanning electron microscopy and hot wet KOH etching, the identified inhomogeneities are found to fall in four categories. Labeled here as type I through IV, they are distinguishable by their size, density, energy, intensity, radiative and electronic characteristics and chemical etch pits which correlates them with dislocations. Type I exhibits a blueshift of about 120 meV for the InGaN quantum well emission attributed to a perturbation of the active region, which is related to indium droplets that form on the surface in the metal-rich InGaN growth condition. Specifically, we attribute the blueshift to a decreased growth rate of and indium incorporation in the InGaN quantum wells underneath the droplet which is postulated to be the result of reduced incorporated N species due to increased N$_{2}$ formation. The location of droplets are correlated with mixed type dislocations for type I defects. Types II through IV are due to screw dislocations, edge dislocations, and dislocation bunching, respectively, and form dark spots due to leakage current and nonradiative recombination.

cond-mat.mtrl-sci↗

arXiv2020

Crystal orientation dictated epitaxy of ultrawide bandgap 5.4-8.6 eV $α$-(AlGa)$_2$O$_3$ on m-plane sapphire

Ultra-wide bandgap semiconductors are ushering in the next generation of high power electronics. The correct crystal orientation can make or break successful epitaxy of such semiconductors. Here it is discovered that single-crystalline layers of $α$-(AlGa)$_2$O$_3$ alloys spanning bandgaps of 5.4 - 8.6 eV can be grown by molecular beam epitaxy. The key step is found to be the use of m-plane sapphire crystal. The phase transition of the epitaxial layers from the $α$- to the narrower bandgap $β$-phase is catalyzed by the c-plane of the crystal. Because the c-plane is orthogonal to the growth front of the m-plane surface of the crystal, the narrower bandgap pathways are eliminated, revealing a route to much wider bandgap materials with structural purity. The resulting energy bandgaps of the epitaxial layers span a range beyond the reach of all other semiconductor families, heralding the successful epitaxial stabilization of the largest bandgap materials family to date.

physics.app-ph↗

arXiv2019

Enhanced injection efficiency and light output in bottom tunnel-junction light-emitting diodes using UID GaN spacers

Recently, the use of bottom-TJ geometry in LEDs, which achieves N-polar-like alignment of polarization fields in conventional metal-polar orientations, has enabled enhancements in LED performance due to improved injection efficiency. Here, we elucidate the root causes behind the enhanced injection efficiency by employing mature laser diode structures with optimized heterojunction GaN/In$_{0.17}$Ga$_{0.83}$N/GaN TJs and UID GaN spacers to separate the optical mode from the heavily doped absorbing p-cladding regions. In such laser structures, polarization offsets at the electron blocking layer, spacer, and quantum barrier interfaces play discernable roles in carrier transport. By comparing a top-TJ structure to a bottom-TJ structure, and correlating features in the electroluminescence, capacitance-voltage, and current-voltage characteristics to unique signatures of the N- and Ga-polar polarization heterointerfaces in energy band diagram simulations, we identify that improved hole injection at low currents, and improved electron blocking at high currents, leads to higher injection efficiency and higher output power for the bottom-TJ device throughout 5 orders of current density (0.015 - 1000 A/cm$^2$). Moreover, even with the addition of a UID GaN spacer, differential resistances are state-of-the-art, below 7x10-4 $Ω$cm$^2$. These results highlight the virtues of the bottom-TJ geometry for use in high-efficiency laser diodes.

physics.app-ph↗

arXiv2019

Do People Prefer "Natural" code?

Natural code is known to be very repetitive (much more so than natural language corpora); furthermore, this repetitiveness persists, even after accounting for the simpler syntax of code. However, programming languages are very expressive, allowing a great many different ways (all clear and unambiguous) to express even very simple computations. So why is natural code repetitive? We hypothesize that the reasons for this lie in fact that code is bimodal: it is executed by machines, but also read by humans. This bimodality, we argue, leads developers to write code in certain preferred ways that would be familiar to code readers. To test this theory, we 1) model familiarity using a language model estimated over a large training corpus and 2) run an experiment applying several meaning preserving transformations to Java and Python expressions in a distinct test corpus to see if forms more familiar to readers (as predicted by the language models) are in fact the ones actually written. We find that these transformations generally produce program structures that are less common in practice, supporting the theory that the high repetitiveness in code is a matter of deliberate preference. Finally, 3) we use a human subject study to show alignment between language model score and human preference for the first time in code, providing support for using this measure to improve code.

cs.CL↗

arXiv2019

Enabling Intuitive Human-Robot Teaming Using Augmented Reality and Gesture Control

Human-robot teaming offers great potential because of the opportunities to combine strengths of heterogeneous agents. However, one of the critical challenges in realizing an effective human-robot team is efficient information exchange - both from the human to the robot as well as from the robot to the human. In this work, we present and analyze an augmented reality-enabled, gesture-based system that supports intuitive human-robot teaming through improved information exchange. Our proposed system requires no external instrumentation aside from human-wearable devices and shows promise of real-world applicability for service-oriented missions. Additionally, we present preliminary results from a pilot study with human participants, and highlight lessons learned and open research questions that may help direct future development, fielding, and experimentation of autonomous HRI systems.

cs.RO↗

arXiv2019

Unlocking Social Media and User Generated Content as a Data Source for Knowledge Management

The pervasiveness of Social Media and user-generated content has triggered an exponential increase in global data volumes. However, due to collection and extraction challenges, data in many feeds, embedded comments, reviews and testimonials are inaccessible as a generic data source. This paper incorporates Knowledge Management framework as a paradigm for knowledge management and data value extraction. This framework embodies solutions to unlock the potential of UGC as a rich, real-time data source for analytical applications. The contributions described in this paper are threefold. Firstly, a method for automatically navigating pagination systems to expose UGC for collection is presented. This is evaluated using browser emulation integrated with dynamic data collection. Secondly, a new method for collecting social data without any a priori knowledge of the sites is introduced. Finally, a new testbed is developed to reflect the current state of internet sites and shared publicly to encourage future research. The discussion benchmarks the new algorithm alongside existing data extraction techniques and provides evidence of the increased amount of UGC data made accessible by the new algorithm.

cs.SI↗

arXiv2018

A Review of Dynamic Network Models with Latent Variables

We present a selective review of statistical modeling of dynamic networks. We focus on models with latent variables, specifically, the latent space models and the latent class models (or stochastic blockmodels), which investigate both the observed features and the unobserved structure of networks. We begin with an overview of the static models, and then we introduce the dynamic extensions. For each dynamic model, we also discuss its applications that have been studied in the literature, with the data source listed in Appendix. Based on the review, we summarize a list of open problems and challenges in dynamic network modeling with latent variables.

stat.ME↗

arXiv2018

An Empirical Analysis of Traceability in the Monero Blockchain

Monero is a privacy-centric cryptocurrency that allows users to obscure their transactions by including chaff coins, called "mixins," along with the actual coins they spend. In this paper, we empirically evaluate two weaknesses in Monero's mixin sampling strategy. First, about 62% of transaction inputs with one or more mixins are vulnerable to "chain-reaction" analysis -- that is, the real input can be deduced by elimination. Second, Monero mixins are sampled in such a way that they can be easily distinguished from the real coins by their age distribution; in short, the real input is usually the "newest" input. We estimate that this heuristic can be used to guess the real input with 80% accuracy over all transactions with 1 or more mixins. Next, we turn to the Monero ecosystem and study the importance of mining pools and the former anonymous marketplace AlphaBay on the transaction volume. We find that after removing mining pool activity, there remains a large amount of potentially privacy-sensitive transactions that are affected by these weaknesses. We propose and evaluate two countermeasures that can improve the privacy of future transactions.

cs.CR↗

arXiv2017

High efficiency deep-UV emission at 219 nm from ultrathin MBE GaN/AlN quantum heterostructures

Deep ultraviolet (UV) optical emission below 250 nm (~5 eV) in semiconductors is traditionally obtained from high aluminum containing AlGaN alloy quantum wells. It is shown here that high-quality epitaxial ultrathin binary GaN quantum disks embedded in an AlN matrix can produce efficient optical emission in the 219-235 nm (~5.7 to 5.3 eV) spectral range, far above the bulk bandgap (3.4 eV) of GaN. The quantum confinement energy in these heterostructures is larger than the bandgaps of traditional semiconductors, made possible by the large band offsets. These MBE-grown extreme quantum-confinement GaN/AlN heterostructures exhibit internal quantum efficiency as high as 40% at wavelengths as short as 219 nm. These observations, together with the ability to engineer the interband optical matrix elements to control the direction of photon emission in such new binary quantum disk active regions offers unique advantages over alloy AlGaN quantum well counterparts for the realization of deep-UV light-emitting diodes and lasers.

cond-mat.mes-hall↗

arXiv2017

MBE-grown 232-270 nm Deep-UV LEDs using Monolayer thin Binary GaN/AlN quantum heterostructures

Electrically injected deep ultra-violet (UV) emission is obtained using monolayer (ML) thin GaN/AlN quantum structures as active regions. The emission wavelength is tuned by controlling the thickness of ultrathin GaN layers with monolayer precision using plasma assisted molecular beam epitaxy (PAMBE). Single peaked emission spectra is achieved with narrow full width at half maximum (FWHM) for three different light emitting diodes (LEDs) operating at 232 nm, 246 nm and 270 nm. 232 nm (5.34 eV) is the shortest EL emission wavelength reported so far using GaN as the light emitting material and employing polarization-induced doping.

cond-mat.mtrl-sci↗

arXiv2015

Nonparametric mixture of Gaussian graphical models

Graphical model has been widely used to investigate the complex dependence structure of high-dimensional data, and it is common to assume that observed data follow a homogeneous graphical model. However, observations usually come from different resources and have heterogeneous hidden commonality in real-world applications. Thus, it is of great importance to estimate heterogeneous dependencies and discover subpopulation with certain commonality across the whole population. In this work, we introduce a novel regularized estimation scheme for learning nonparametric mixture of Gaussian graphical models, which extends the methodology and applicability of Gaussian graphical models and mixture models. We propose a unified penalized likelihood approach to effectively estimate nonparametric functional parameters and heterogeneous graphical parameters. We further design an efficient generalized effective EM algorithm to address three significant challenges: high-dimensionality, non-convexity, and label switching. Theoretically, we study both the algorithmic convergence of our proposed algorithm and the asymptotic properties of our proposed estimators. Numerically, we demonstrate the performance of our method in simulation studies and a real application to estimate human brain functional connectivity from ADHD imaging data, where two heterogeneous conditional dependencies are explained through profiling demographic variables and supported by existing scientific findings.

stat.ME↗

arXiv2002

Study of the Backgrounds for the Search for Proton Decay to 10^35 Y at the WIPP Site with the LANNDD Detector

We briefly describe the LANNDD 70-kT liquid argon TPC proposal for the WIPP underground facility at Carlsbad, New Mexico. We, then, identify the key backgrounds for the search for p -> K+ nu_bar to 10^35 years lifetime. The most serious non-neutrino background is due to high-energy neutrons producing strange particles in the detector. We show that this can be reduced to an acceptable level by appropriate fiducial volume cuts.

astro-ph↗