DeepSense

Yao, Shuochao; Hu, Shaohan; Zhao, Yiran; Zhang, Aston; Abdelzaher, Tarek

doi:10.1145/3038912.3052577

Cited by 494 publications

(85 citation statements)

References 30 publications

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“…Moreover, we experimented with a time series length of 13 time stamps×50 ms each time stamp = 0.65 s. Higher frequencies might affect architectures' performance and result in even higher classification accuracies [38]. Yet, leveraging longer time series for user classification implies that we have to rely on longer authentication times.…”

Section: Limitationsmentioning

confidence: 99%

Knowledge-driven Biometric Authentication in Virtual Reality

Mathis

Fawaz

Khamis

2020

Extended Abstracts of the 2020 CHI Conference on Human Factors in Computing Systems

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With the increasing adoption of virtual reality (VR) in public spaces, protecting users from observation attacks is becoming essential to prevent attackers from accessing context-sensitive data or performing malicious payment transactions in VR. In this work, we propose RubikBiom, a knowledge-driven behavioural biometric authentication scheme for authentication in VR. We show that hand movement patterns performed during interactions with a knowledgebased authentication scheme (e.g., when entering a PIN) can be leveraged to establish an additional security layer. Based on a dataset gathered in a lab study with 23 participants, we show that knowledge-driven behavioural biometric authentication increases security in an unobtrusive way. We achieve an accuracy of up to 98.91% by applying a Fully Convolutional Network (FCN) on 32 authentications per subject. Our results pave the way for further investigations towards knowledge-driven behavioural biometric authentication in VR.

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Section: Limitationsmentioning

confidence: 99%

Knowledge-driven Biometric Authentication in Virtual Reality

Mathis

Fawaz

Khamis

2020

Extended Abstracts of the 2020 CHI Conference on Human Factors in Computing Systems

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“…Other studies focus on different deep learning models for time series forecasting. Indeed, new trends are emerging regarding the use of Convolutional Neural Networks (CNNs) (Cai et al, 2019;Rahimilarki et al, 2019;Yao et al, 2017) and attention mechanisms (Serra et al, 2018;Vaswani et al, 2017). One study, performed by Cai et al (2019), focused on deep learning-based techniques, in particular RNNs and CNNs, for day-ahead multi-step load forecasting in commercial buildings, comparing the obtained results with ARIMAX.…”

Section: The Literaturementioning

confidence: 99%

Long Short-Term Memory Networks for Traffic Flow Forecasting: Exploring Input Variables, Time Frames and Multi-Step Approaches

Fernandes¹,

Silva²,

Aláiz‐Moretón³

et al. 2020

Informatica

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Traffic flow forecasting is an acknowledged time series problem whose solutions have been essentially grounded on statistical-based models. Recent times came, however, with promising results regarding the use of Recurrent Neural Networks (RNNs), such as Long Short-Term Memory networks (LSTMs), to accurately address time series problems. Literature is, however, evasive in regard to several aspects of the conceived models and often exhibits misconceptions that may lead to important pitfalls. This study aims to conceive and find the best possible LSTM model for traffic flow forecasting while addressing several important aspects of such models such as the multitude of input features, the time frames used by the model and the employed approach for multi-step forecasting. To overcome the spatial problem of open source datasets, this study presents and describes a new dataset collected by the authors of this work. After several weeks of model fitting, Recursive Multi-Step Multi-Variate models were the ones showing better performance, strengthening the perception that LSTMs can be used to accurately forecast the traffic flow for several future timesteps.

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“…To maximize the information that can be derived from a one-second sound, we propose a Convolutional Neural Network (CNN). CNNs have already been successfully applied to different audio event detection applications [1,5,63,83]. For BEM to identify breath sound, we employ a single layer CNN to extract information from the entire one-second sound features.…”

Section: Breathing Phases Detection Modulementioning

confidence: 99%

Breeze

Shih

Tomita

Lukic

et al. 2019

Proc. ACM Interact. Mob. Wearable Ubiquitous Technol.

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Slow-paced biofeedback-guided breathing training has been shown to improve cardiac functioning and psychological wellbeing. Current training options, however, attract only a fraction of individuals and are limited in their scalability as they require dedicated biofeedback hardware. In this work, we present Breeze, a mobile application that uses a smartphone's microphone to continuously detect breathing phases, which then trigger a gamified biofeedback-guided breathing training. Circa 2.76 million breathing sounds from 43 subjects and control sounds were collected and labeled to train and test our breathing detection algorithm. We model breathing as inhalation-pause-exhalation-pause sequences and implement a phase-detection system with an attention-based LSTM model in conjunction with a CNN-based breath extraction module. A biofeedback-guided breathing training with Breeze takes place in real-time and achieves 75.5% accuracy in breathing phases detection. Breeze was also evaluated in a pilot study with 16 new subjects, which demonstrated that the majority of subjects prefer Breeze over a validated active control condition in its usefulness, enjoyment, control, and usage intentions. Breeze is also effective for strengthening users' cardiac functioning by increasing high-frequency heart rate variability. The results of our study suggest that Breeze could potentially be utilized in clinical and self-care activities. Sailboat In h a le P a u se E xh a le P a u se … Nose Mouth R e p e a te d a co u st ic b re a th in g se q u e n ce Fig. 1. Overview of Breeze, a mobile gamified biofeedback breathing training.

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DeepSense

Cited by 494 publications

References 30 publications

Knowledge-driven Biometric Authentication in Virtual Reality

Knowledge-driven Biometric Authentication in Virtual Reality

Long Short-Term Memory Networks for Traffic Flow Forecasting: Exploring Input Variables, Time Frames and Multi-Step Approaches

Breeze

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