Unmanned Aerial Vehicles (UAVs) have achieved considerable popularity due to their maneuverability and versatility. However, UAV swarms often face challenges from external malicious jamming and internal co-channel interference when performing reconnaissance missions. Aiming to counteract malicious jamming and co-channel interference, effectively enhancing the anti-jamming transmission capability of UAV networks, this paper proposes UAV deployment schemes based on congestion game model, which adjust the transmitting power of UAV while optimizing the deployment position. By defining the utility function of the UAVs and analyzing the conditions for achieving Nash equilibrium, the optimal deployment strategy and power strategy are realized. Experimental results show that this method effectively improves the data collection efficiency and power efficiency of UAV swarms.

High mobility feature in communication scenario encourages the emergence of the orthogonal time frequency space (OTFS) modulation. The Cyclic prefix (CP) in OTFS systems is used to combat Inter-Symbol Interference (ISI) and Intercarrier Interference (ICI), which simplifies the design of the equalizer by restoring circulant convolution relationship between symbols and the channel. However, the overhead of CP weighs the burden of spectral efficiency and latency. In this paper, an improved CP-free OTFS framework is designed combined with a dual-component Weighted Fractional Fourier transform (DCWFRFT) precoder and an enhanced detector. DCWFRFT precoder enables more evenly distributed signal energy to achieve better performance. To ensure the reliability of CP-free OTFS, the enhanced detector firstly truncates the ISI contaminated portion and then reconstructs these symbols from the reliable one. Finally, the reliable portion and reconstructed portion are combined to be detected and decoded. Furthermore, the normalized mean square error (NMSE) and bit error rate (BER) performance of the proposed detector and CP-free OTFS system are simulated. Results show that the proposed OTFS framework improves spectral efficiency by discarding CP while maintaining satisfactory BER performance compared with CP-OTFS.

Cloud computing is on the rise, which promotes new breeds of database systems to accommodate the cloud environment. The development of cloud-native databases reveals three trends. One is the adoption of multi-datacenter (DC) deployment to survive the downtime of any single site. Another is the separation of computation and storage resources to achieve higher elasticity and scalability. The last is the support of HTAP to eliminate data redundancy and system complexity from heterogeneous databases. To cater to these trends, we design a distributed relational database called PolarDB-X, which is built on top of the cloud-native database PolarDB. It hence inherits many cloud-native features, such as multi-datacenter deployment and elasticity. To achieve cross-DC capability, it leverages Paxos and hybrid logical clock to achieve durability and snapshot-isolation consistency with low coordination costs. For resource elasticity, since the underlying PolarDB supports rapid migration of tenants between nodes, PolarDB-X can quickly scale the cluster to cope with a sudden traffic increase. For HTAP support, with the help of read replicas and a HTAP executor, PolarDB-X can improve the latency and parallelism of analytical queries without impacting concurrently-running TP workloads. Using its MPP engine and an in-memory column index, the efficiency of analytical queries can be further enhanced. PolarDB-X is now a cloud database service at Alibaba Cloud. We have learned many useful lessons from its development and operation, and have incorporated those into our design and analysis.

Large-scale pre-trained models' demand for high-quality instances forces people to consider how to select instances for annotation with limited resources. Nonetheless, little attention has been paid to the scenario where the number of instances that ultimately need to be annotated is agnostic. Meanwhile, the anisotropy of the sentence vector output by pre-trained models makes it hard to represent the instance itself well. Faced with the two challenges, we propose an incrementally few-shot instance selection approach (TIMS) based on model similarity and outlier detection, which suits the starting step of active learning well and serves as a better benchmark for few-shot learning. Specifically, TIMS determines the representative candidate set by calculating the similarity between changes in model parameters caused by each instance and by the full dataset. Meanwhile, Isolation Forest is adopted to select instances from the candidate set for annotation, which prevents selected instances from being too similar. Comprehensive experiments on WikiLingua & SQuAD show that TIMS outperforms other algorithms across almost every circumstance. It inspires us that the proper implementation of model similarity detection and outlier detection is of great help to select representative instances incrementally.

Due to its excellent ability in learning features from large scale raw data, deep learning (DL) has attracted much attention for encrypted traffic classification. However, most DL-based traffic classifiers usually rely on enormous labeled samples. Motivated by this, we investigate a self-supervised traffic classifier (FLAG) without sacrifice of identification accuracy, only depending on small labeled traffic samples and highly available unlabeled traffic samples. Specifically, focusing on local short-term characteristics of traffic, we design a preprocessing algorithm, termed as N-phrase Extration, to convert unlabeled raw traffic dataset into sequences of high-frequency phrases as input of Bidirectional Encoder. On account of their significance, potential timing characteristics from input sequences are mined by Bidirectional Encoder and embedded into robust representations with distributed vectors to enhance classifier’s performance significantly. Our comprehensive experiments indicate FLAG can achieve 98.65% in 100% of dataset and 98.07% in 10% of dataset in terms of true positive rate in UNB ISCX VPN-nonVPN dataset, which are better than p-FP, FS-Net and Deep Packet.

The detection of the electric network frequency (ENF) in digital recordings is an essential step before the subsequent ENF extraction and forensic analysis. In this letter, we extend the state-of-the-art single-tone time-frequency (TF) domain ENF detector to the multi-tone scenario and propose a multi-harmonic combining (MHC) method, exploiting ENF harmonic components for improved detection performance. To exclude the corrupted components interfering rather than contributing to ENF detection, the proposed detector first performs a pre-screening based on the estimated average subband signal-to-noise ratios (SNRs) to exclude interfering components. Then, with the selected harmonic candidates, a second screening process is applied based on the TF test statistics (TSs), i.e., the variances of observed subband traces. After that, the multi-harmonic components are combined to form the final TS, whose sign determines the final decision. The advantages of the proposed method are illustrated via both synthetic analysis and real-world experimental results using the ENF-WHU dataset.

Recently, it has been discovered that the electric network frequency (ENF) could be captured by digital audio, video, or even image files, and could further be exploited in forensic investigations. However, the existence of the ENF in multimedia content is not a sure thing, and if the ENF is not present, ENF-based forensic analysis would become useless or even misleading. In this paper, we address the problem of ENF detection in digital audio recordings, which is modeled as the detection of a weak (ENF) signal contaminated by unknown colored wide-sense stationary (WSS) Gaussian noise, while the signal also contains multiple unknown random parameters. We first derive three Neyman-Pearson (NP) detectors, i.e., general matched filter (GMF), matched filter (MF)-like detector, and the asymptotic approximation of the GMF, and choose the MF-like detector as the clairvoyant detector. For practical detectors, we show that the generalized likelihood ratio test (GLRT) could not be efficiently obtained due to the unknown noise and large matrix inversion. Alternatively, we propose two least-squares (LS)-based time domain detectors termed as LS-likelihood ratio test (LRT) and naive-LRT. Further, we propose a time-frequency (TF) domain detector, termed as TF detector, which exploits the a priori knowledge of the ENF. The performances of the derived detectors are extensively analyzed in terms of test statistic distributions, threshold selection, and computational complexity. The naive-LRT detector is found to be only effective for very short recordings. As the data recording length increases, both LS-LRT and TF detectors yield effective detection results, while the latter is approximately a constant false alarm rate (CFAR) detector. Practical experiments using real audio recordings justify the effectiveness of the proposed detectors and our analysis.

The electric network frequency (ENF) is an important and extensively researched forensic criterion to authenticate digital recordings, but currently it is still challenging to extract reliable ENF traces from recordings in uncontrollable environments. In this paper, we present a framework for robust ENF extraction from real-world audio recordings, featuring multi-tone harmonic ENF enhancement and graph-based harmonic selection. We first extend the recently developed single-tone robust filtering algorithm (RFA) to the multi-tone scenario and propose a harmonic robust filtering algorithm (HRFA). It can enhance each harmonic component without cross-component interference, thus alleviating the effects of unwanted noise and audio content. In addition, considering the fact that some harmonic components could still be severely corrupted after the HRFA, interfering rather than facilitating ENF estimation, we propose a graph-based harmonic selection algorithm (GHSA), which finds a subset of harmonic components having the overall highest mutual cross-correlation. Noticeably, the harmonic selection problem is found to be equivalent to the maximum weight clique problem in graph theory, and the Bron-Kerbosch algorithm is adopted in the GHSA. With the enhanced and carefully selected harmonic components, both the existing maximum likelihood estimator (MLE) and weighted MLE are incorporated to yield the final ENF estimation results. The proposed framework is evaluated using both synthetic signals and the ENF-WHU dataset consisting of 130 real-world audio recordings, demonstrating its advantages over both the existing single- and multi-tone competitors. This work further improves the applicability of the ENF as a forensic criterion in real-world situations.