A Hybrid Deep Learning System for Real-World Mobile User Authentication Using Motion Sensors

Zhu, Tiantian; Weng, Zhengqiu; Guo-lang, Chen; Fu, Lei

doi:10.3390/s20143876

Cited by 20 publications

(33 citation statements)

References 56 publications

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“…The original data set for this experiment included 910 values; the unbalanced data were processed with the SMOTE algorithm [ 34 ], and the overall number of values increased to 1527. The data were randomly assigned to the training set and the test set at a ratio of 8:2 [ 35 ]. The training set was used to train the SVM model, and the test set was used for verification.…”

Section: Resultsmentioning

confidence: 99%

Research on a Dynamic Algorithm for Cow Weighing Based on an SVM and Empirical Wavelet Transform

Ningning

Han

et al. 2020

Sensors

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Weight is an important indicator of the growth and development of dairy cows. The traditional static weighing methods require considerable human and financial resources, and the existing dynamic weighing algorithms do not consider the influence of the cow motion state on the weight curve. In this paper, a dynamic weighing algorithm for cows based on a support vector machine (SVM) and empirical wavelet transform (EWT) is proposed for classification and analysis. First, the dynamic weight curve is obtained by using a weighing device placed along a cow travel corridor. Next, the data are preprocessed through valid signal acquisition, feature extraction, and normalization, and the results are divided into three active degrees during motion for low, medium, and high grade using the SVM algorithm. Finally, a mean filtering algorithm, the EWT algorithm, and a combined periodic continuation-EWT algorithm are used to obtain the dynamic weight values. Weight data were collected for 910 cows, and the experimental results displayed a classification accuracy of 98.6928%. The three algorithms were used to calculate the dynamic weight values for comparison with real values, and the average error rates were 0.1838%, 0.6724%, and 0.9462%. This method can be widely used at farms and expand the current knowledgebase regarding the dynamic weighing of cows.

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Section: Resultsmentioning

confidence: 99%

Research on a Dynamic Algorithm for Cow Weighing Based on an SVM and Empirical Wavelet Transform

Ningning

Han

et al. 2020

Sensors

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“…A total of 15 studies related to privacy and security corresponding to APC, PPS, and MDPS sub-research themes mainly used the user interaction data (i.e., type/target of interaction, UI changed) [56,55,50,49,51,131,132,57,133,60,59,134,58,135,136]. By contrast, surveyed studies belong to ASS (i.e., Authentication System/Scheme) sub-research themes used the context and system sensing data along with user interaction data for the research related to the user authentication system and scheme to understand the current device hold situation and daily habits of the user or to develop a motion sensor-based authentication system [52,53,54,128,129,130].…”

Section: Privacy and Security (Ps) Research Themesmentioning

confidence: 99%

A Systematic Survey on Android API Usage for Data-Driven Analytics with Smartphones

Lee,

Park,

Lee

2021

Preprint

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Recently, there has been an increase in industrial and academic research on data-driven analytics with smartphones based on the collection of app usage patterns and surrounding context data. The Android mobile operating system utilizes Usage Statistics API (US API) and Accessibility Service API (AS API) as representative APIs to passively collect app usage data. These APIs are used for various research purposes as they can collect app usage patterns (e.g., app status, usage time, app name, user interaction state, and smartphone use state) and fine-grained data (e.g., user interface elements & hierarchy and user interaction type & target & time) of each application. In addition, other sensing APIs help to collect the user's surroundings context (location, network, ambient environment) and device state data, along with AS/US API. In this review, we provide insights on the types of mobile usage and sensor data that can be collected for each research purpose by considering Android built-in APIs and sensors (AS/US API, and other sensing APIs). Moreover, we classify the research purposes of the surveyed papers into four categories and 17 sub-categories, and create a hierarchical structure for data classification, comprising three layers. We present the important trends in the usage of Android's built-in APIs and sensors, including AS/US API, the types of data collected using the presented APIs, and discuss the utilization of mobile usage and sensor data in future research.

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“…Deep learning is a particularly popular research method nowadays. It can learn the internal laws and representation levels of sample data through multi-layer neural networks, and it has been widely used in many fields in recent years [ 38 , 39 , 40 , 41 , 42 , 43 ]. At the same time, neural networks are also considered as a useful method for unsteady aerodynamic modeling.…”

Section: Artificial Neural Network Modelsmentioning

confidence: 99%

Modeling of a Novel Coaxial Ducted Fan Aerial Robot Combined with Corner Environment by Using Artificial Neural Network

Chen

et al. 2020

Sensors

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A novel coaxial ducted fan aerial robot with a manipulator is proposed which can achieve some hover operation tasks in a corner environment, such as switching on and off a wall-attached button on the corner. In order to study the aerodynamic interference between the prototype and the environment when the aerial robot is hovering in the corner environment, a method for the comprehensive modeling of the prototype and corner environment based on the artificial neural network is presented. By using the CFD simulation software, the flow field of the prototype at different positions with the corner effect is analyzed. After determining the input, output and structure of the neural network model, the Adam and gradient descent algorithms are selected as the neural network training algorithms, respectively. In addition, to optimize the initial weights and biases of the neural network model, the genetic algorithm is precisely used. The three-dimensional prediction surfaces generated by the three methods of the neural network, kriging surface and the polynomial fitting are compared. The results show that the neural network has high prediction accuracy, and can be applied to the comprehensive modeling of the prototype and the corner environment.

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A Hybrid Deep Learning System for Real-World Mobile User Authentication Using Motion Sensors

Cited by 20 publications

References 56 publications

Research on a Dynamic Algorithm for Cow Weighing Based on an SVM and Empirical Wavelet Transform

Research on a Dynamic Algorithm for Cow Weighing Based on an SVM and Empirical Wavelet Transform

A Systematic Survey on Android API Usage for Data-Driven Analytics with Smartphones

Modeling of a Novel Coaxial Ducted Fan Aerial Robot Combined with Corner Environment by Using Artificial Neural Network

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