Mobile Devices based Eavesdropping of Handwriting

Yu, Tuo; Jin, Haiming; Nahrstedt, Klara

doi:10.1109/tmc.2019.2912747

Cited by 10 publications

(7 citation statements)

References 24 publications

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“…LSTM is a type of recurrent neutral network (RNN). LSTM achieved great success in many applications, such as unconstrained handwriting recognition [46], speech recognition [47], handwriting generation [35], machine translation [48], etc. Each step of the LSTM has a series of repeated neural network templates.…”

Section: Inter-atomic Long-dependence Feature Extraction Methods Basedmentioning

confidence: 99%

Critical Temperature Prediction of Superconductors Based on Atomic Vectors and Deep Learning

Dan

et al. 2020

Symmetry

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In this paper, a hybrid neural network (HNN) that combines a convolutional neural network (CNN) and long short-term memory neural network (LSTM) is proposed to extract the high-level characteristics of materials for critical temperature (Tc) prediction of superconductors. Firstly, by obtaining 73,452 inorganic compounds from the Materials Project (MP) database and building an atomic environment matrix, we obtained a vector representation (atomic vector) of 87 atoms by singular value decomposition (SVD) of the atomic environment matrix. Then, the obtained atom vector was used to implement the coded representation of the superconductors in the order of the atoms in the chemical formula of the superconductor. The experimental results of the HNN model trained with 12,413 superconductors were compared with three benchmark neural network algorithms and multiple machine learning algorithms using two commonly used material characterization methods. The experimental results show that the HNN method proposed in this paper can effectively extract the characteristic relationships between the atoms of superconductors, and it has high accuracy in predicting the Tc.Symmetry 2020, 12, 262 2 of 13 same time, the prediction of the Tc of superconductors, especially high-temperature superconductors, is not very accurate. The solution of these problems depends on the discovery of superconductors or similar materials and an understanding of the physical properties of these materials. Although this was the focus of research for the past 30 years, the prediction of Tc of superconductors is still very difficult.Advances in computers, as well as the development and continuous improvement of first-principles computational quantum chemistry theories and statistical (or machine learning) methods, greatly influenced research activities related to material discovery and design [12]. Material design for high-throughput (HT) calculations made progress in determining the structure of thousands of inorganic solids [13,14]. Since 1970, density functional theory (DFT) is widely used in the calculation of solid-state physics. In most cases, compared with other methods for solving multi-body problems in quantum mechanics, DFT using local density approximation gives very satisfactory results, and solid-state computing is less expensive than experiments. DFT is the leading method for the calculation of electronic structures in various fields; however, these methods are currently not suitable for high-level calculations due to the high-cost calculation. When using standard exchanges and correlation functions such as Perdew-Berke-Ernzerhof (PBE) [15] which is currently the most widely used exchange-related functional form in the calculation of solid structures, there are cases where the system is underestimated compared to the experimental values.In addition to prediction models based on physical principles/theories, the machine learning [16-21] approach for Tc prediction is a data-driven prediction model, which exploits the relationship between material ...

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Section: Inter-atomic Long-dependence Feature Extraction Methods Basedmentioning

confidence: 99%

Critical Temperature Prediction of Superconductors Based on Atomic Vectors and Deep Learning

Dan

et al. 2020

Symmetry

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“…We fuse IMU with UWB, resulting in a robust level accuracy. Acoustic: Acoustic signal has been emerging as a rising technique for tracking [8,12,28,35,37,52,[60][61][62]. Strata [63] achieves 8 error by tracking the position from the phase shift of the audio signal.…”

Section: Related Workmentioning

confidence: 99%

ITrackU

Cao

Dhekne

Ammar

2021

Proceedings of the 19th Annual International Conference on Mobile Systems, Applications, and Services

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High-precision tracking of a pen-like instrument's movements is desirable in a wide range of fields spanning education, robotics, and art, to name a few. The key challenge in doing so stems from the impracticality of embedding electronics in the tip of such instruments (a pen, marker, scalpel, etc.) as well as the difficulties in instrumenting the surface that it works on. In this paper, we present ITrackU, a movement digitization system that does not require modifications to the surface or the tracked instrument's tip. ITrackU fuses locations obtained using ultra-wideband radios (UWB), with an inertial and magnetic unit (IMU) and a pressure sensor, yielding multidimensional improvements in accuracy, range, cost, and robustness, over existing works. ITrackU embeds a micro-transmitter at the base of a pen which creates a trackable beacon, that is localized from the corners of a writing surface. Fused with inertial motion sensor and a pressure sensor, ITrackU enables accurate tracking. Our prototype of ITrackU covers a large 2.5 × 2 area, while obtaining around 2.9 median error. We demonstrate the accuracy of our system by drawing numerous shapes and characters on a whiteboard, and compare them against a touchscreen and a camera-based ground-truthing system. Finally, the produced stream of digitized data is minuscule in volume, when compared with a video of the whiteboard, which saves both network bandwidth and storage space. CCS CONCEPTS• Computer systems organization → Sensors and actuators; • Human-centered computing → Ubiquitous and mobile devices.

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“…Authors claim that there is a good chance to achieve 50-60% of word recognition under certain conditions. This work has been extended to also include a hand motion tracking method to enhance the performance of the eavesdropping, thus improving performance to 70-80% [68]. All these types of attacks show that security and privacy for smart healthcare are of extreme importance for the viability of smart healthcare.…”

Section: Security and Privacy For Smart Healthcarementioning

confidence: 99%

Emerging Wireless Sensor Networks and Internet of Things Technologies—Foundations of Smart Healthcare

Gardasevic

Katzis

Bajić

et al. 2020

Sensors

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Future smart healthcare systems—often referred to as Internet of Medical Things (IoMT) – will combine a plethora of wireless devices and applications that use wireless communication technologies to enable the exchange of healthcare data. Smart healthcare requires sufficient bandwidth, reliable and secure communication links, energy-efficient operations, and Quality of Service (QoS) support. The integration of Internet of Things (IoT) solutions into healthcare systems can significantly increase intelligence, flexibility, and interoperability. This work provides an extensive survey on emerging IoT communication standards and technologies suitable for smart healthcare applications. A particular emphasis has been given to low-power wireless technologies as a key enabler for energy-efficient IoT-based healthcare systems. Major challenges in privacy and security are also discussed. A particular attention is devoted to crowdsourcing/crowdsensing, envisaged as tools for the rapid collection of massive quantities of medical data. Finally, open research challenges and future perspectives of IoMT are presented.

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Mobile Devices based Eavesdropping of Handwriting

Cited by 10 publications

References 24 publications

Critical Temperature Prediction of Superconductors Based on Atomic Vectors and Deep Learning

Critical Temperature Prediction of Superconductors Based on Atomic Vectors and Deep Learning

ITrackU

Emerging Wireless Sensor Networks and Internet of Things Technologies—Foundations of Smart Healthcare

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