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Deep learning (DL) has emerged as a key application exploiting the increasing computational power in systems such as GPUs, multicore processors, Systems-on-Chip (SoC), and distributed clusters. It has also attracted much attention in discovering correlation patterns in data in an unsupervised manner and has been applied in various domains including speech recognition, image classification, natural language processing, and computer vision. Unlike traditional machine learning (ML) approaches, DL also enables dynamic discovery of features from data. In addition, now, a number of commercial vendors also offer accelerators for deep learning systems (such as Nvidia, Intel, and Huawei).

In the domain of password security, research has made significant progress in handling different kinds of threats which require human intelligence factor to fix the vulnerabilities. In spite of having strong theoretical establishments, most of these defense mechanisms cannot be used in practice as humans have limitations in processing complex information. The little bit of good news is that very few research proposals in this field have shown the promises to be deployable in practice. This paper focuses on such one method - proposed by Roth et al. back in 2004, which provides adequate user-friendliness to enter Personal Identification Number (PIN) securely in the presence of human shoulder surfers. Surprisingly, the background algorithm of this method for validating users’ responses runs in linear time on a search space of cardinality 5 and hence, the validation process does not put much load on the authenticating device. Therefore, such human identification protocol can also be integrated into the IoT infrastructure for conducting a more secured login from the client-side. Having such advantages, though remained secure for almost ten years after its release in 2004, recently, few proposals revealed some serious vulnerable aspects of the Roth et al. ’s proposal. In this paper, we have taken an attempt to save this user-friendly form of authentication. Firstly, we have made a critical discussion on the importance of the targeted PIN entry method in the domain of usable security and then given a brief overview of the identified limitations of this protocol. Followed by this, a few initiatives have been taken to fix the identified vulnerabilities of Roth et al. ’s proposal by revising its working principle, while the login procedure and the usability standard of this method stay unaffected.

The accuracy transmission of a binary offset carrier (BOC) signal is always affected by narrowband interference (NBI), and a novel anti-jamming acquisition algorithm based on all-phase processing finite impulse response (apFIR) and partially matched filter-all phase fast Fourier transform (PMF-apFFT) is proposed in this paper. First, an apFIR filter with precise settings for arbitrary notch points was constructed, and then the PMF-apFFT method was adopted to achieve the acquisition of the contaminated BOC signals. Simulation and analysis show that the proposed algorithm can effectively suppress NBI and simultaneously complete correct pseudo-random (PN) phase search and accurate Doppler frequency estimation, and it shows superior performance in mean square error (MSE) and detection probability.

Accurately determining power consumer harmonic contribution determination is an effective method to solve power quality disputes and alleviate harmonic pollution of power grids. This paper proposes a multi-harmonic sources harmonic contribution determination algorithm based on data filtering and cluster analysis. Aiming at the problem of background harmonic fluctuation, this paper uses the cross-approximation entropy (CAE) algorithm to filter the effective data segments of the harmonic voltage and current at the point of common coupling (PCC) to avoid the interference caused by background harmonics. For the problem of harmonic impedance changes of system, using the density-based spatial clustering of applications with noise (DBSCAN) clustering algorithm to detect the harmonic impedance changes of the system. The harmonic contribution under different system harmonic impedance is calculated based on the data of each class cluster. The accuracy of the proposed method is improved compared with existing methods. The experimental analysis demonstrates the effectiveness and superiority of the algorithm.

As the coupling and integration of multi-energy flow in the integrated energy system (IES) deepen increasingly, the cascading failure will develop across different energy systems more easily and widely through the energy hub (EH). And it brings great challenges to the security and reliability of IES. The defects of the present cascading failure model of IES have been summarized, and a novel search strategy of fault chains in IES combined heating and power network was proposed in this paper. First, the initial risk assessment index of each energy branch is proposed to form the initial fault sets. Then, combined heat and power control (CHPC) strategies are introduced to deal with the branch overload conditions during the cascading failure. What is more, in order to reduce the workload and overcome the limitation of present methods, we analyzed the relevance of the branches to be predicted by using the kernel fuzzy C-means (KFCM) clustering algorithm and select the branches with the highest value of relevance as the subsequent failure. Based on the predicted fault chain, vulnerability analysis is presented to locate critical component and find out the correlation between cascading outages. The comprehensive evaluation index is also established to effectively evaluate the impact severity of cascading failure. Finally, the case studies are carried out on the combined heating and power systems to demonstrate the effectiveness of the proposed method.