Aggregate signatures allow someone to aggregate multiple signatures into one signature, which is suitable for resource-constrained and computationally inefficient environments. Identify-based aggregate signature can solve the storage problem of public key certificates while achieving efficient signature verification. However, in most of the identity-based aggregate signature schemes, the user identity revocation process is time-consuming and cannot resist quantum attacks. To solve above problems, this paper proposes a lattice-based aggregate signature scheme with dynamic identity revocation by combining lattice-based cryptography and an aggregate signature scheme. The security of the proposed lattice-based aggregate signature scheme with dynamic identity revocation has been proved in the random oracle model. In addition, the verification efficiency of the aggregate signature has been improved compared with multiple different signatures. Much of the data transfer in Vehicular Ad Hoc Network (VANET) is carried out wirelessly, which makes VANET vulnerable to identity spoofing attacks. Identity authentication technology can prevent attackers from impersonating legitimate users, thus ensuring the security of VANET. Based on the proposed lattice-based aggregate signature scheme with dynamic identity revocation, this paper proposes a lattice-based batch authentication scheme with dynamic identity revocation in VANET. Through the proposed batch authentication scheme, we can effectively resist the impersonation attack of VANET in the quantum computer environment, and the efficiency of authentication is improved.

Epilepsy is one of the most common neurological disorders, and there exists a subset of patients with refractory epilepsy that require surgical removal of the epileptogenic foci (EF) area. Studies have shown that high-frequency oscillations (HFOs) in epileptic electroencephalogram signals can be used as an essential biomarker for locating EF. This paper proposes a new method for rapid localization of EF based on the automatic detection of HFOs by waveform feature templates (WFTs). First, the initial screening of HFOs based on Hilbert transform and subsequent rescreening with short-time energy and short-time Fourier transform is performed, and the two screening results are used as the template data set of HFOs. Then, a coarse-grained and fine-grained screening method for detecting HFOs using autocorrelation coefficients and interrelation coefficients as WFT detectors, respectively. Compared with the Hilbert transform detector and other HFOs detector methods proposed at abroad in recent years, the experimental simulations showed that the automatic detector based on WFT could detect HFOs more rapidly, accurately, and efficiently. Our proposed WFT detector has the advantages of high specificity, high sensitivity, and high accuracy in locating EF and has a high clinical utility.

Federated Learning (FL) enables the deployment of distributed machine learning models over the cloud and Edge Devices (EDs) while preserving the privacy of sensitive local data, such as electronic health records. However, despite FL advantages regarding security and flexibility, current constructions still suffer from some limitations. Namely, heavy computation overhead on limited resources EDs, communication overhead in uploading converged local models' parameters to a centralized server for parameters aggregation, and lack of guaranteeing the acquired knowledge preservation in the face of incremental learning over new local data sets. This paper introduces a secure and resource-friendly protocol for parameters aggregation in federated incremental learning and its applications. In this study, the central server relies on a new method for parameters aggregation called orthogonal gradient aggregation. Such a method assumes constant changes of each local data set and allows updating parameters in the orthogonal direction of previous parameters spaces. As a result, our new construction is robust against catastrophic forgetting, maintains the federated neural network accuracy, and is efficient in computation and communication overhead. Moreover, extensive experiments analysis over several significant data sets for incremental learning demonstrates our new protocol's efficiency, efficacy, and flexibility.

Epilepsy is a disease caused by abnormal discharges in the central nervous system. Automatic detection and accurate identification of epileptic seizures based on electroencephalography (EEG) are significant in the clinical diagnosis and treatment of epilepsy. In this paper, we first decompose the patient's EEG signal into multiple intrinsic modal functions (IMFs) using empirical modal decomposition, then compute the mean, standard deviation, fluctuation index, and sample entropy of IMF1, and finally classify them using a fusion algorithm of support vector machine and K-nearest neighbor optimized by particle swarm algorithm. The results of validation using the epileptic EEG data set from Bonn University show that the auto-detection and fast recognition method proposed in this paper can achieve a high seizure accuracy recognition rate (≥95%) with only a small number of training samples, which has a good clinical application value.

Indoor positioning systems are becoming increasing popular recently. While most existing indoor positioning studies focus on improving accuracy, less attention is paid to the latency problem. The traditional fusion algorithm uses the received signal strength-based (RSS-Based) positioning result to correct the current position of the system. However, it fails to keep up with the actual moving speed of the user during the navigation. The location retrospective adjustment (LRA) method proposed in this paper uses the RSS-Based positioning result to correct the past position of the system, which can effectively eliminate positioning delay and improve the real-time response of navigation. We tested in a 70 m linear promenade and found that the addition of LRA results in a reduction of the positioning error around −0.3 to +0.4 m, which improves 85%. Additionally, the LRA method alleviates the requirements for the immediate response of RSS positioning, and the RSS positioning algorithm can be moved to the cloud. It reduces the download resources and computing load on the mobile phone. The complete indoor navigation application is presented in HTML5 which allows users to navigate without having to download the APP in advance, and it takes only 4–9 s for users to launch the application for the first time. We tested the application in a hospital with a total floor area of 79,000 m2 in 7 buildings. The system achieves an average positioning accuracy of 0.65 m at a long navigation distance of 220 m. To our knowledge, this paper is the first to consider the latency issue in indoor navigation. The proposed LRA approach improves real-time navigation performance, lightens the computation load on the mobile phone, and allows cloud-based positioning systems to provide stable and accurate navigation even under poor network quality in crowded areas.

A large number of people suffer from anxiety in modern society. As an effective treatment with few side effects, music therapy has been used to reduce anxiety for decades in clinical practice. Yet therapists continue to perform music selection, a key step in music therapy, manually. Considering the growing need for music therapy services and social distancing amid public emergencies, an automatic method for music selection would be of great practical utility. This paper marks the first effort to identify music with therapeutic effects on anxiety reduction via a novel music scoring model. We formulate the calculation of a therapeutic score as a quadratic programming problem, which minimizes score variance among known therapeutic songs while maintaining their superiority over other songs. The proposed model can uncover common features that contribute to anxiety reduction by learning from small and unbalanced data. Using a music therapy experiment, we find that the proposed model outperforms existing techniques in predicting therapeutic songs. Feature analysis is also conducted, revealing that high-frequency spectrums are important in therapeutic scoring.