Leave Your Phone at the Door

Hojjati, Avesta; Adhikari, Anku; Struckmann, Katarina M; Chou, Edward; Nguyen, Thi Ngoc Tho; Madan, Kushagra; Winslett, Marianne; Gunter, Carl A.; King, William P.

doi:10.1145/2976749.2978323

Cited by 51 publications

(7 citation statements)

References 16 publications

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“…These same clusters are highly efficient at lensing background galaxies, hence our cross-correlation measurement would be sensitive to this effect. Indeed, the tSZ × γ t , as recently measured in Hojjati et al (2016), has a very large signal to noise and could possibly be detected in a targeted analysis. Although it is difficult to assess the exact level of the tSZ signal in our κ CMB map, the cleaning made possible from the multi-frequency observations from Planck is thorough, reducing the residual contaminants to a very small fraction.…”

Section: Planck κ Cmb Mapsmentioning

confidence: 92%

“…The signal-tonoise (SNR) ratio is given by the likelihood ratio test, which measures the confidence at which we can reject the null hypothesis (i.e. that there is no signal, simply noise) in favour of an alternative hypothesis described by our theoretical model with a single parameter A (see Hojjati et al 2016, for a recent derivation in a similar context). We Table 2.…”

Section: Significancementioning

confidence: 99%

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KiDS-450: tomographic cross-correlation of galaxy shear with Planck lensing

Harnois-Déraps

Tröster

Chisari

et al. 2017

Monthly Notices of the Royal Astronomical Society

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We present the tomographic cross-correlation between galaxy lensing measured in the Kilo Degree Survey (KiDS-450) with overlapping lensing measurements of the cosmic microwave background (CMB), as detected by Planck 2015. We compare our joint probe measurement to the theoretical expectation for a flat ΛCDM cosmology, assuming the best-fitting cosmological parameters from the KiDS-450 cosmic shear and Planck CMB analyses. We find that our results are consistent within 1σ with the KiDS-450 cosmology, with an amplitude re-scaling parameter A KiDS = 0.86 ± 0.19. Adopting a Planck cosmology, we find our results are consistent within 2σ, with A Planck = 0.68 ± 0.15. We show that the agreement is improved in both cases when the contamination to the signal by intrinsic galaxy alignments is accounted for, increasing A by ∼ 0.1. This is the first tomographic analysis of the galaxy lensing -CMB lensing cross-correlation signal, and is based on five photometric redshift bins. We use this measurement as an independent validation of the multiplicative shear calibration and of the calibrated source redshift distribution at high redshifts. We find that constraints on these two quantities are strongly correlated when obtained from this technique, which should therefore not be considered as a stand-alone competitive calibration tool.Key words: Gravitational lensing: weak -Large scale structure of Universe -Dark matter c 2016 RAS 3 www.lsst.org 4 sci.esa.int/euclid 5 Our fiducial cosmology consists of a flat ΛCDM universe in which the dark energy equation of state is set to w = −1.

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Section: Planck κ Cmb Mapsmentioning

confidence: 92%

Section: Significancementioning

confidence: 99%

KiDS-450: tomographic cross-correlation of galaxy shear with Planck lensing

Harnois-Déraps

Tröster

Chisari

et al. 2017

Monthly Notices of the Royal Astronomical Society

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“…In AM production, as well as implementing strategies discussed in Sections 4.1 and 4.2, additional tools have been developed to aid in general production planning using NNs, and manufacturability through a variety of methods. In addition, research has been conducted on ML implementations to replicate CAD geometry from acoustic signals produced during manufacturing, creating concerns around data security [54,55].…”

Section: Machine Learning For Additive Manufacturing Productionmentioning

confidence: 99%

“…The vulnerability of material extrusion machines to intellectual property theft was identified by Al Faruque et al [54] and Hojjati et al [55]. Al Faruque et al [54] showed that the noise emitted by stepper motors during printing can be recorded and processed to infer features of the print process:…”

Section: Printability and Dimensional Deviation Managementmentioning

confidence: 99%

Machine Learning for Additive Manufacturing

2021

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Additive manufacturing (AM) is the name given to a family of manufacturing processes where materials are joined to make parts from 3D modelling data, generally in a layer-upon-layer manner. AM is rapidly increasing in industrial adoption for the manufacture of end-use parts, which is therefore pushing for the maturation of design, process, and production techniques. Machine learning (ML) is a branch of artificial intelligence concerned with training programs to self-improve and has applications in a wide range of areas, such as computer vision, prediction, and information retrieval. Many of the problems facing AM can be categorised into one or more of these application areas. Studies have shown ML techniques to be effective in improving AM design, process, and production but there are limited industrial case studies to support further development of these techniques.

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“…They can also be used to infer the user's gait patterns [15], the activity being performed [16] even location and travel routes [17], [18]. The accelerometers have also been used to infer information outside the context of the device, effectively using it as a network enabled sensor [19], [20].…”

Section: Introductionmentioning

confidence: 99%

Reconstructing What You Said: Text Inference Using Smartphone Motion

Hodges

Buckley

2019

IEEE Trans. on Mobile Comput.

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Smartphones and tablets are becoming ubiquitous within our connected lives and as a result these devices are increasingly being used for more and more sensitive applications, such as banking. The security of the information within these sensitive applications is managed through a variety of different processes, all of which minimise the exposure of this sensitive information to other potentially malicious applications. This paper documents experiments with the 'zero-permission' motion sensors on the device as a side-channel for inferring the text typed into a sensitive application. These sensors are freely accessible without the phone user having to give permission. The research was able to, on average, identify nearly 30% of typed bigrams from unseen words, using a very small volume of training data, which was less than the size of a tweet. Given the natural redundancy in language this performance is often enough to understand the phrase being typed. We found that large devices were typically more vulnerable, as were users who held the device in one hand whilst typing with fingers. Of those bigrams which were not correctly identified over 60% of the errors involved the space bar and nearly half of the errors are within two keys on the keyboard.

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Leave Your Phone at the Door

Cited by 51 publications

References 16 publications

KiDS-450: tomographic cross-correlation of galaxy shear with Planck lensing

KiDS-450: tomographic cross-correlation of galaxy shear with Planck lensing

Machine Learning for Additive Manufacturing

Reconstructing What You Said: Text Inference Using Smartphone Motion

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