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  1. 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.
    Free, publicly-accessible full text available April 1, 2023
  2. Deploying unmanned aerial vehicle (UAV) mounted base stations with a renewable energy charging infrastructure in a temporary event (e.g., sporadic hotspots for light reconnaissance mission or disaster-struck areas where regular power-grid is unavailable) provides a responsive and cost-effective solution for cellular networks. Nevertheless, the energy constraint incurred by renewable energy (e.g., solar panel) imposes new challenges on the recharging coordination. The amount of available energy at a charging station (CS) at any given time is variable depending on: the time of day, the location, sunlight availability, size and quality factor of the solar panels used, etc. Uncoordinated UAVs make redundant recharging attempts and result in severe quality of service (QoS) degradation. The system stability and lifetime depend on the coordination between the UAVs and available CSs. In this paper, we develop a reinforcement learning time-step based algorithm for the UAV recharging scheduling and coordination using a Q-Learning approach. The agent is considered a central controller of the UAVs in the system, which uses the ϵ -greedy based action selection. The goal of the algorithm is to maximize the average achieved throughput, reduce the number of recharging occurrences, and increase the life-span of the network. Extensive simulations based on experimentally validatedmore »UAV and charging energy models reveal that our approach exceeds the benchmark strategies by 381% in system duration, 47% reduction in the number of recharging occurrences, and achieved 66% of the performance in average throughput compared to a power-grid based infrastructure where there are no energy limitations on the CSs.« less
    Free, publicly-accessible full text available November 29, 2022
  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.
    Free, publicly-accessible full text available November 1, 2022
  4. Smart grids integrate advanced information and communication technologies (ICTs) into traditional power grids for more efficient and resilient power delivery and management, but also introduce new security vulnerabilities that can be exploited by adversaries to launch cyber attacks, causing severe consequences such as massive blackout and infrastructure damages. Existing machine learning-based methods for detecting cyber attacks in smart grids are mostly based on supervised learning, which need the instances of both normal and attack events for training. In addition, supervised learning requires that the training dataset includes representative instances of various types of attack events to train a good model, which is sometimes hard if not impossible. This paper presents a new method for detecting cyber attacks in smart grids using PMU data, which is based on semi-supervised anomaly detection and deep representation learning. Semi-supervised anomaly detection only employs the instances of normal events to train detection models, making it suitable for finding unknown attack events. A number of popular semi-supervised anomaly detection algorithms were investigated in our study using publicly available power system cyber attack datasets to identify the best-performing ones. The performance comparison with popular supervised algorithms demonstrates that semi-supervised algorithms are more capable of finding attack eventsmore »than supervised algorithms. Our results also show that the performance of semi-supervised anomaly detection algorithms can be further improved by augmenting with deep representation learning.« less
  5. 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.
  6. Abstract Background The maize inbred line A188 is an attractive model for elucidation of gene function and improvement due to its high embryogenic capacity and many contrasting traits to the first maize reference genome, B73, and other elite lines. The lack of a genome assembly of A188 limits its use as a model for functional studies. Results Here, we present a chromosome-level genome assembly of A188 using long reads and optical maps. Comparison of A188 with B73 using both whole-genome alignments and read depths from sequencing reads identify approximately 1.1 Gb of syntenic sequences as well as extensive structural variation, including a 1.8-Mb duplication containing the Gametophyte factor1 locus for unilateral cross-incompatibility, and six inversions of 0.7 Mb or greater. Increased copy number of carotenoid cleavage dioxygenase 1 ( ccd1 ) in A188 is associated with elevated expression during seed development. High ccd1 expression in seeds together with low expression of yellow endosperm 1 ( y1 ) reduces carotenoid accumulation, accounting for the white seed phenotype of A188. Furthermore, transcriptome and epigenome analyses reveal enhanced expression of defense pathways and altered DNA methylation patterns of the embryonic callus. Conclusions The A188 genome assembly provides a high-resolution sequence for a complexmore »genome species and a foundational resource for analyses of genome variation and gene function in maize. The genome, in comparison to B73, contains extensive intra-species structural variations and other genetic differences. Expression and network analyses identify discrete profiles for embryonic callus and other tissues.« less
    Free, publicly-accessible full text available December 1, 2022
  7. Home area networks (HANs) are the most vulnerable part of smart grids since they are not directly controlled by utilities. Device authentication is one of most important mechanisms to protect the security of smart grid-enabled HANs (SG-HANs). In this paper, we propose a situation-aware scheme for efficient device authentication in SG-HANs. The proposed scheme utilizes the security risk information assessed by the smart home system with a situational awareness feature. A suitable authentication protocol with adequate security protection and computational and communication complexity is then selected based on the assessed security risk level. A protocol design of the proposed scheme considering two security risk levels is presented in the paper. The security of the design is verified by using both formal verification and informal security analysis. Our performance analysis demonstrates that the proposed scheme is efficient in terms of computational and communication costs.