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  1. Free, publicly-accessible full text available December 1, 2024
  2. User authentication plays an important role in securing systems and devices by preventing unauthorized accesses. Although surface Electromyogram (sEMG) has been widely applied for human machine interface (HMI) applications, it has only seen a very limited use for user authentication. In this paper, we investigate the use of multi-channel sEMG signals of hand gestures for user authentication. We propose a new deep anomaly detection-based user authentication method which employs sEMG images generated from multi-channel sEMG signals. The deep anomaly detection model classifies the user performing the hand gesture as client or imposter by using sEMG images as the input. Different sEMG image generation methods are studied in this paper. The performance of the proposed method is evaluated with a high-density hand gesture sEMG (HD-sEMG) dataset and a sparse-density hand gesture sEMG (SD-sEMG) dataset under three authentication test scenarios. Among the sEMG image generation methods, root mean square (RMS) map achieves significantly better performance than others. The proposed method with RMS map also greatly outperforms the reference method, especially when using SD-sEMG signals. The results demonstrate the validity of the proposed method with RMS map for user authentication. 
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  3. User authentication is an important security mechanism to prevent unauthorized accesses to systems or devices. In this paper, we propose a new user authentication method based on surface electromyogram (sEMG) images of hand gestures and deep anomaly detection. Multi-channel sEMG signals acquired during the user performing a hand gesture are converted into sEMG images which are used as the input of a deep anomaly detection model to classify the user as client or imposter. The performance of different sEMG image generation methods in three authentication test scenarios are investigated by using a public hand gesture sEMG dataset. Our experimental results demonstrate the viability of the proposed method for user authentication. 
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  4. null (Ed.)
    Transfer learning using pre-trained deep neural networks (DNNs) has been widely used for plant disease identification recently. However, pre-trained DNNs are susceptible to adversarial attacks which generate adversarial samples causing DNN models to make wrong predictions. Successful adversarial attacks on deep learning (DL)-based plant disease identification systems could result in a significant delay of treatments and huge economic losses. This paper is the first attempt to study adversarial attacks and detection on DL-based plant disease identification. Our results show that adversarial attacks with a small number of perturbations can dramatically degrade the performance of DNN models for plant disease identification. We also find that adversarial attacks can be effectively defended by using adversarial sample detection with an appropriate choice of features. Our work will serve as a basis for developing more robust DNN models for plant disease identification and guiding the defense against adversarial attacks. 
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  5. null (Ed.)
  6. Pattern unlock is a popular screen unlock scheme that protects the sensitive data and information stored in mobile devices from unauthorized access. However, it is also susceptible to various attacks, including guessing attacks, shoulder surfing attacks, smudge attacks, and side-channel attacks, which can achieve a high success rate in breaking the patterns. In this paper, we propose a new two-factor screen unlock scheme that incorporates surface electromyography (sEMG)-based biometrics with patterns for user authentication. sEMG signals are unique biometric traits suitable for person identification, which can greatly improve the security of pattern unlock. During a screen unlock session, sEMG signals are recorded when the user draws the pattern on the device screen. Time-domain features extracted from the recorded sEMG signals are then used as the input of a one-class classifier to identify the user is legitimate or not. We conducted an experiment involving 10 subjects to test the effectiveness of the proposed scheme. It is shown that the adopted time-domain sEMG features and one-class classifiers achieve good authentication performance in terms of the F 1 score and Half of Total Error Rate (HTER). The results demonstrate that the proposed scheme is a promising solution to enhance the security of pattern unlock. 
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  7. Abstract

    In this study, a quadruply nested, nonhydrostatic tropical cyclone (TC) model is used to investigate how the structure and intensity of a mature TC respond differently to imposed lower‐layer and upper‐layer unidirectional environmental vertical wind shears (VWSs). Results show that TC intensity in both cases decrease shortly after the VWS is imposed but with quite different subsequent evolutions. The TC weakens much more rapidly for a relatively long period in the upper‐layer shear than in the lower‐layer shear, which is found to be related to the stronger storm‐relative asymmetric flow in the middle‐upper troposphere and the larger vertical vortex tilt in the former than in the latter. The stronger storm‐relative flow in the former imposes a greater ventilation of the warm core in the middle‐upper troposphere, leading to a more significant weakening of the storm. The storm in the lower‐layer shear only weakens initially after the VWS is imposed but then experiences a quasi periodic intensity oscillation with a period of about 24 hr. This quasi periodic behavior is found to be closely related to the boundary layer thermodynamic “discharge/recharge” mechanism associated with the activity of shear‐induced outer spiral rainbands. There is no significant intensity oscillation for the storm embedded in the upper‐layer shear, even though outer spiral rainbands develop quasi periodically also. The boundary layer inflow is very weak in that case and the low equivalent potential temperature air induced by downdrafts in outer spiral rainbands therefore cannot penetrate into the inner core but remains in the outer region.

     
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