Transfer Learning for Motor Imagery Based Brain-Computer Interfaces: A Complete Pipeline

Transfer learning (TL) has been widely used in motor imagery (MI) based brain-computer interfaces (BCIs) to reduce the calibration effort for a new subject, and demonstrated promising performance. While a closed-loop MI-based BCI system, after electroencephalogram (EEG) signal acquisition and temporal filtering, includes spatial filtering, feature engineering, and classification blocks before sending out the control signal to an external device, previous approaches only considered TL in one or two such components. This paper proposes that TL could be considered in all three components (spatial filtering, feature engineering, and classification) of MI-based BCIs. Furthermore, it is also very important to specifically add a data alignment component before spatial filtering to make the data from different subjects more consistent, and hence to facilitate subsequential TL. Offline calibration experiments on two MI datasets verified our proposal. Especially, integrating data alignment and sophisticated TL approaches can significantly improve the classification performance, and hence greatly reduces the calibration effort

CryptoEval: Evaluating the Risk of Cryptographic Misuses in Android Apps with Data-Flow Analysis

The misunderstanding and incorrect configurations of cryptographic primitives have exposed severe security vulnerabilities to attackers. Due to the pervasiveness and diversity of cryptographic misuses, a comprehensive and accurate understanding of how cryptographic misuses can undermine the security of an Android app is critical to the subsequent mitigation strategies but also challenging. Although various approaches have been proposed to detect cryptographic misuses in Android apps, seldom studies have focused on estimating the security risks introduced by cryptographic misuses. To address this problem, we present an extensible framework for deciding the threat level of cryptographic misuses in Android apps. Firstly, we propose a unified specification for representing cryptographic misuses to make our framework extensible and develop adapters to unify the detection results of the state-of-the-art cryptographic misuse detectors, resulting in an adapter-based detection toolchain for a more comprehensive list of cryptographic misuses. Secondly, we employ a misuse-originating data-flow analysis to connect each cryptographic misuse to a set of data-flow sinks in an app, based on which we propose a quantitative data-flow-driven metric for assessing the overall risk of the app introduced by cryptographic misuses. To make the per-app assessment more useful in the app vetting at the app-store level, we apply unsupervised learning to predict and classify the top risky threats, to guide more efficient subsequent mitigations. In the experiments on an instantiated implementation of the framework, we evaluate the accuracy of our detection and the effect of data-flow-driven risk assessment of our framework. Our empirical study on over 40,000 apps as well as the analysis of popular apps reveals important security observations on the real threats of cryptographic misuses in Android apps

EEG-based brain-computer interfaces are vulnerable to backdoor attacks

Research and development of electroencephalogram (EEG) based brain-computer interfaces (BCIs) have advanced rapidly, partly due to deeper understanding of the brain and wide adoption of sophisticated machine learning approaches for decoding the EEG signals. However, recent studies have shown that machine learning algorithms are vulnerable to adversarial attacks. This paper proposes to use narrow period pulse for poisoning attack of EEG-based BCIs, which makes adversarial attacks much easier to implement. One can create dangerous backdoors in the machine learning model by injecting poisoning samples into the training set. Test samples with the backdoor key will then be classified into the target class specified by the attacker. What most distinguishes our approach from previous ones is that the backdoor key does not need to be synchronized with the EEG trials, making it very easy to implement. The effectiveness and robustness of the backdoor attack approach is demonstrated, highlighting a critical security concern for EEG-based BCIs and calling for urgent attention to address it

The Prevalence of Immunologic Injury in Renal Allograft Recipients with De Novo Proteinuria

Post-transplant proteinuria is a common complication after renal transplantation; it is associated with reduced graft and recipient survival. However, the prevalence of histological causes has been reported with considerable variation. A clinico-pathological re-evaluation of post-transplant proteinuria is necessary, especially after dismissal of the term “chronic allograft nephropathy,” which had been considered to be an important cause of proteinuria. Moreover, urinary protein can promote interstitial inflammation in native kidney, whether this occurs in renal allograft remains unknown. Factors that affect the graft outcome in patients with proteinuria also remain unclear. Here we collected 98 cases of renal allograft recipients who developed proteinuria after transplant, histological features were characterized using Banff scoring system. Cox proportional hazard regression models were used for graft survival predictors. We found that transplant glomerulopathy was the leading (40.8%) cause of post-transplant proteinuria. Immunological causes, including transplant glomerulopathy, acute rejection, and chronic rejection accounted for the majority of all pathological causes of proteinuria. Nevertheless, almost all patients that developed proteinuria had immunological lesions in the graft, especially for interstitial inflammation. Intraglomerular C3 deposition was unexpectedly correlated with the severity of proteinuria. Moreover, the severity of interstitial inflammation was an independent risk factor for graft loss, while high level of hemoglobin was a protective factor for graft survival. This study revealed a predominance of immunological parameters in renal allografts with post-transplant proteinuria. These parameters not only correlate with the severity of proteinuria, but also with the outcome of the graft

Numerical simulation of bubble dynamics in an elastic vessel

The nonlinear evolution of a gas bubble in the middle of an elastic vessel is investigated numerically. The fluids inside and outside the vessel are assumed to be incompressible and potential. A boundary element method (BEM) is adopted to solve the Laplace equation for the velocity potential. The gas inside the bubble is described by the Boyle Law. The fluid outside the vessel is assumed to contain the elasticity. The dynamic boundary condition on the vessel interface i is obtained from the derived Bernoulli Equation. This numerical model is validated through the comparisons with both the analytic solution of Rayleigh-Plesset equation and spark bubble experiment. The bubble dynamic behavior with different elastic parameters and vessel inner radius is further discussed

DeepCatra: Learning flow‐ and graph‐based behaviours for Android malware detection

Abstract As Android malware grows and evolves, deep learning has been introduced into malware detection, resulting in great effectiveness. Recent work is considering hybrid models and multi‐view learning. However, they use only simple features, limiting the accuracy of these approaches in practice. This study proposes DeepCatra, a multi‐view learning approach for Android malware detection, whose model consists of a bidirectional LSTM (BiLSTM) and a graph neural network (GNN) as subnets. The two subnets rely on features extracted from statically computed call traces leading to critical APIs derived from public vulnerabilities. For each Android app, DeepCatra first constructs its call graph and computes call traces reaching critical APIs. Then, temporal opcode features used by the BiLSTM subnet are extracted from the call traces, while flow graph features used by the GNN subnet are constructed from all call traces and inter‐component communications. We evaluate the effectiveness of DeepCatra by comparing it with several state‐of‐the‐art detection approaches. Experimental results on over 18,000 real‐world apps and prevalent malware show that DeepCatra achieves considerable improvement, for example, 2.7%–14.6% on the F1 measure, which demonstrates the feasibility of DeepCatra in practice

DeepCatra: Learning Flow- and Graph-based Behaviors for Android Malware Detection

As Android malware is growing and evolving, deep learning has been introduced into malware detection, resulting in great effectiveness. Recent work is considering hybrid models and multi-view learning. However, they use only simple features, limiting the accuracy of these approaches in practice. In this paper, we propose DeepCatra, a multi-view learning approach for Android malware detection, whose model consists of a bidirectional LSTM (BiLSTM) and a graph neural network (GNN) as subnets. The two subnets rely on features extracted from statically computed call traces leading to critical APIs derived from public vulnerabilities. For each Android app, DeepCatra first constructs its call graph and computes call traces reaching critical APIs. Then, temporal opcode features used by the BiLSTM subnet are extracted from the call traces, while flow graph features used by the GNN subnet are constructed from all the call traces and inter-component communications. We evaluate the effectiveness of DeepCatra by comparing it with several state-of-the-art detection approaches. Experimental results on over 18,000 real-world apps and prevalent malware show that DeepCatra achieves considerable improvement, e.g., 2.7% to 14.6% on F1-measure, which demonstrates the feasibility of DeepCatra in practice.Comment: IET Information Security (to appear

Study on Interaction of Aromatic Substances and Correlation between Electroencephalogram Correlates of Odor Perception in Light Flavor Baijiu

Light-flavor Baijiu (LFB) is widely cherished for its flavor. This study identified the thresholds of 14 aroma compounds in a 52% ethanol-water matrix and conducted a comprehensive analysis of the interactions among key aroma compounds in LFB using the Feller additive model and odor activity values approach. Among them, the interactions of beta-damascenone with ester and alcohol compounds were primarily promotive, while the interaction with acid compounds was predominantly masking. Furthermore, for the first time, the electroencephalogram (EEG) technology was used to characterize the interactions between aroma compounds. The results showed that the brain activity in the alpha frequency band demonstrated heightened olfactory sensitivity. The EEG could not only display the additive effect of odor intensity but also reflect the differences in aroma similarity between different odors. This study demonstrated that the EEG can serve as an effective tool for olfactory assessment