Applied nonparametric statistics , Applied nonparametric statistics , مرکز فناوری اطلاعات و اطلاع رسانی کشاورزی

Applied Nonparametric Statistics

This paper focuses on spectrum sensing under Laplacian noise. To remit the negative effects caused by heavy-tailed behavior of Laplacian noise, the fractional lower order moments (FLOM) technology is employed to pre-process the received samples before spectrum sensing. Via exploiting the asymmetrical difference between the distribution for the FLOM of received samples in the absence and presence of primary users, we formulate the spectrum sensing problem under Laplacian noise as a unilateral goodness-of-fit (GoF) test problem. Based on this test problem, we propose a new GoF-based detector which is called unilateral left-tail Anderson Darling (ULAD) detector. The analytical expressions for the theoretical performance, in terms of false-alarm and detection probabilities, of the ULAD are derived. Moreover, a closed-form expression for the optimal detection threshold is also derived to minimize the total error rate. Simulation results are provided to validate the theoretical analyses and to demonstrate the superior performance of the proposed detector than others.

Unilateral Left-Tail Anderson Darling Test Based Spectrum Sensing with Laplacian Noise

Attributed networks are prevalent in the current information infrastructure, where node attributes enhance knowledge discovery. Anomaly detection in attributed networks is gaining attention for its potential uses in cybersecurity, finance, and healthcare. Recognizing the complicated relationship between node attributes and network topology is crucial for attributed network embedding and anomaly detection. Nevertheless, there are few approaches available to directly represent the relationship between these two perspectives of the node property and the network topology. Approaches utilizing the reconstruction error rely on straightforward, simple mappings, which introduce a substantial risk of overfitting in high-dimensional data, wherein the model acquires patterns that are exclusive to the training data and fails to generalize to new data. To do this, we suggest a new way to find graph anomalies on attributed networks using random masking and padding along with sparse canonical correlation analysis. Motivated by the limitations of existing methodologies in effectively addressing these challenges, our research introduces a novel methodology for anomaly detection in attributed networks by leveraging Sparse Canonical Correlation Analysis (SCCA) in conjunction with Random Masking and Padding (RMP). This dual approach uniquely addresses the challenges of high-dimensional data and the sparsity of attributes, which are prevalent issues in anomaly detection. Unlike previous works that primarily focus on either dimensionality reduction or attribute sparsity independently, our method synergizes these aspects to enhance detection performance. Initially, we randomly mask and pad nodes in the attributed network and use the Graph Convolutional Network (GCN) to map them to latent space. Next, we optimize the distribution alignment of node attributes and graph structure latent space representations by using Kullback-Leibler (KL) divergence regularization, which increases their comparability. Finally, we use sparse canonical correlation analysis (SCCA) to quantify the correlation between node attributes and network structure views in latent space. SCCA incorporates sparsity by making the model choose fewer variables, which adds another level of complexity. It improves interpretability and reduces overfitting in high-dimensional data analysis by highlighting only the key variables. To optimize our model, we maximize the correlation between attribute and structural aspects of normal nodes, and anomalies are detected by measuring the correlation between these two views. Our approach is the first of its kind to provide a novel remedy to the fundamental problems preventing efficient and accurate anomaly identification, thereby establishing a new standard in this field. The proposed model has been extensively tested on four real-world datasets, and its effectiveness has been demonstrated in comparison to state-of-the-art approaches. The empirical evaluation across multiple benchmark datasets validates the potential of the proposed approach as a pivotal tool in advancing anomaly detection research and applications.

Detecting Anomalies in Attributed Networks through Sparse Canonical Correlation Analysis combined with Random Masking and Padding

This study focuses on spectrum sensing under Laplacian noise. To mitigate the negative effects caused by the heavy-tailed behaviour of Laplacian noise, the fractional lower order moments (FLOM) technology is employed to pre-process the received samples before spectrum sensing. Through exploiting the asymmetrical difference between the distribution for the FLOM of received samples in the absence and presence of primary users, the authors formulate the spectrum sensing problem under Laplacian noise as a unilateral goodness-of-fit (GoF) test problem. Based on this test problem, they propose a new GoF-based detector, which is called a unilateral left-tail Anderson Darling (ULAD) detector. The analytical expressions for the theoretical performance, in terms of false-alarm and detection probabilities, of the ULAD are derived. Moreover, a closed-form expression for the optimal detection threshold is also derived to minimise the total error rate. Simulation results are provided to validate the theoretical analyses and to demonstrate the superior performance of the proposed detector than others.

/pdf/unilateral-left-tail-anderson-darling-test-based-spectrum-4zoeum4w11.pdf

Unilateral left-tail anderson darling test based spectrum sensing with Laplacian noise

Describing categorised data and the goodness of fit chi‐square test

In diversity-based multiple antenna cognitive radio system, the performance of the existing cooperative spectrum sensing algorithms based on covariance matrix degrades seriously due to the channel correlation. In this paper, we formulate spectrum sensing as a multinomial distribution test problem, and then apply the Discrete Anderson Darling (DAD) test, one of discrete goodness of fit tests, to examining the above problem. The proposed algorithm dubbed DAD sensing is assessed through Monte Carlo simulations. It is shown that the performance of DAD sensing is robust to the antenna correlation and noise uncertainty.

Cooperative spectrum sensing using discrete goodness of fit testing for multi-antenna cognitive radio system

Anomaly detection in attributed networks aims to find anomalous nodes in the network that differ from the behavior pattern of most nodes, and graph neural network provide a way to use fused structural and attribute information. However, existing methods based on Graph Convolutional Network (GCN) detection do not consider the over-smoothing phenomenon of GCN due to the stacks of network layers, which causes significant performance deterioration. To address the above problems, we propose a graph autoencoder-based anomaly detection method for attributed networks: Residual Graph Autoencoder (Res-GAE), by which the performance is effectively improved. Res-GAE contains an encoder and two decoders. More specifically, the encoder consists of a GCN and a residual network is utilized to learn the network representation. The decoders are designed to reconstruct the network structure and node attributes respectively. After that, the objective function is used to analyze the reconstruction error to generate the anomaly score ranking, to realize anomaly detection. Extensive experiments on the three datasets (BlogCatalog, Flickr, ACM) demonstrate that the proposed method has the significant improvement compared with other baseline methods.

A Graph Autoencoder-based Anomaly Detection Method for Attributed Networks

Multiantenna based spectrum sensing algorithms are widely used in cognitive radio networks on account of improving the system reliability. Utilizing the difference between the received signal and the noise statistical covariances, two kinds of novel spectrum sensing algorithms, binomial distribution based detection (BD) and wilcoxon signed rank test based detection (WSD), are proposed based on the sample covariance matrix calculated from a limited number of received signal samples. BD and WSD algorithms do not need any priori information of the primary signal and the noise. In addition, their thresholds are found via the statistical theory. Compared with energy detection (ED), maximum-minimum eigenvalue (MME) and covariance absolute value (CAV), those two algorithms can obtain better performance. Finally, the performance of the proposal is verified by simulations.

Multiantenna Based Blind Spectrum Sensing via Nonparametric Test

Text classification is a longstanding research topic in natural language processing (NLP). Deep learning has emerged as an effective paradigm for solving text classification problems. However, the performance of a deep model is heavily reliant on large-scale human-annotated data. In this paper, we propose a Semi-Supervised Contrastive Learning (SSCL) framework for text classification, which can significantly improve the performance of deep models in the case of limited labeled data. The proposed framework consists of two components: a pseudo label generation strategy and a contrastive learning scheme for text classification. We first devise a prompt-based strategy for training Bidirectional Encoder Representation from Transformers (BERT), with a small amount of human-labeled data, to obtain a task-correlation model capable of generating pseudo labels for unlabeled text. Then, for the text classification task, we use a two-step contrastive learning scheme: pre-training a deep model with pseudo labels as supervision to capture inter-class patterns while mitigating the negative impact of pseudo label noise, and then fine-tuning the pre-trained model with human-labeled data using a supervised contrastive learning approach. Benefiting from the generated pseudo labels and anti-noise contrastive pre-training, we only use a small amount of labeled data during the training process for the downstream text classification tasks. Experimental results on the twitter sentiment classification dataset and the aspect classification dataset show that our method significantly outperforms baseline methods in a few-shot setting.

Few-Shot Text Classification via Semi-Supervised Contrastive Learning

Goodness of Fit tests have been used to find available spectrum with excellent detection performance in Cognitive Radio System. To extend those works, in this paper, we reformulate the spectrum sensing as a unilateral Goodness of Fit testing problem. With difference to previous available works, a random variable that obeys central F distribution with presence of primary user (PU) signal and a non-F distribution with absence of PU signal, which provides technical support for achieving blind spectrum sensing; furthermore, inspired by the thought of unilateral hypothesis test, we apply Right Anderson Darling (RAD) test to achieve bind spectrum sensing and derive a blind spectrum sensing called RAD sensing. Finally, the validness of proposed algorithm is proved by enormous Monte Carlo simulations.

Blind Spectrum Sensing in Cognitive Radio Using Right Anderson Darling Test

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