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Covert Communication in Underlay Multi-Antenna Cognitive Radio Networks

IEEE Transactions on Cognitive Communications and Networking 2024年第3期11卷 1493-1507页

作者： Fan, Hongbin Yang, Weiwei Shi, Hui Pan, Gaofeng Si, Jiangbo Wang, Yutao Rong, Chuanzhen Army Engineering University of PLA College of Communications Engineering Nanjing210007 China Xiangnan University College of Computer and Artificial Intelligence Chenzhou423000 China Guilin University of Electronic Technology Guangxi Key Laboratory of Trusted Software Guilin541004 China Beijing Institute of Technology School of Cyberspace Science and Technology Beijing100081 China Xidian University State Key Laboratory of Integrated Services Networks Xi'an710100 China

In this paper, we investigated a multi-antenna covert communication in underlay cognitive radio networks (UCRNs), by considering both cases that Willie has the perfect and statistical channel state information (CSI) of the link from the secondary transmitter (ST) to the primary receiver (PR), respectively. Specifically, the optimal antenna of the secondary receiver (SR) is selected to receive the private information from ST, and then one of the remaining antennas is randomly selected to transmit jamming signals. The closed-form expressions of Willie's detection error probability, the average minimum detection error probability, the transmission outage probability and the maximum effective covert rate under both cases are derived. The maximum effective covert rate is obtained by optimising the maximum jamming power of SR. The numerical results show that the combined effect of the maximum interference tolerance of PR and the maximum interference power of SR provides the positive effective covert rate between ST and SR. It is further found that the maximum effective covert rate between ST and SR when Willie knows the perfect CSI of the ST-SR link is greater than that when Willie only knows the statistical CSI of the ST-SR link. © 2015 IEEE.

关键词： Jamming

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SemLog: A Semantics-based Approach for Anomaly Detection in Big Data System Logs 29

SemLog: A Semantics-based Approach for Anomaly Detection in ...

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29th IEEE International Conference on Parallel and Distributed Systems, ICPADS 2023

作者： Tan, Xiaoman Han, Ningning Lu, Siyang Chen, Wu Wang, Dongdong College of Computer and Information Science College of Software Southwest University Chongqing China School of Computer and Information Technology Beijing Jiaotong University Beijing China Environmental and Construction Engineering University of Central Florida Department of Civil OrlandoFL United States

ISBN: (纸本)9798350330717

Syslog-based anomaly detection is crucial for protecting the systems from malicious attacks or malfunctions. System logs are semi-structured text messages printed by logging statements to record the system's run-time status, involving rich semantic information. However, the existing BERT-based log anomaly detection method is based on the log key sequence, does not consider the semantics of the log data, and discards the variable part, resulting in a high rate of missed detection. In this paper, we propose SemLog, a self-supervised framework for log anomaly detection based on BERT. By incorporating log semantics and variables and employing multi-feature fusion, we mitigate the independent assumption issue in the Masked Language Modeling model. The experimental results on three benchmarks show that SemLog achieves high performance compared with the state-of-the-art approaches for anomaly detection. © 2023 IEEE.

关键词： BERT log anomaly detection log semantics log variables multi-feature fusion

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Efficient Privacy-Preserving Multi-Functional Data Aggregation Scheme for Multi-Tier IoT System 28

Efficient Privacy-Preserving Multi-Functional Data Aggregati...

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28th IEEE Symposium on computers and Communications, ISCC 2023

作者： Tao, Yunting Kong, Fanyu Shi, Yuliang Yu, Jia Zhang, Hanlin Liu, Huiyi School of Software Shandong University China College of Computer Science and Technology Qingdao University China College of Information Engineering Binzhou Polytechnic China Shandong Sansec Information Technology Co. Ltd China

ISBN: (纸本)9798350300482

The proliferation of Internet of Things (IoT) devices has led to the generation of massive amounts of data that require efficient aggregation for analysis and decision-making. However, multi-tier IoT systems, which involve multiple layers of devices and gateways, face more complex security challenges in data aggregation compared to ordinary IoT systems. In this paper, we propose an efficient privacy-preserving multi-functional data aggregation scheme for multi-tier IoT architecture. The scheme supports privacy-preserving calculation of mean, variance, and anomaly proportion. The scheme uses the Paillier cryptosystem and the BLS algorithm for encryption and signature, and uses blinding techniques to keep the size of the IoT system secret. In order to make the Paillier algorithm more suitable for the IoT scenario, we also improve its efficiency of encryption and decryption. The performance evaluation shows that the scheme improves encryption efficiency by 43.7% and decryption efficiency by 45% compared to the existing scheme. © 2023 IEEE.

关键词： Internet of things

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Drowsy Driving Detection System Using Face Detection 3

Drowsy Driving Detection System Using Face Detection

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3rd International Conference on Technological Advancements in Computational sciences, ICTACS 2023

作者： Mirabdullayev, Ismoiljon Ahamed Ayoobkhan, Mohamed Uvaze Jasmine Hashana, A.M. Khan Subair Ali, Liayakath Ali Software Engineering New Uzbekistan University Tashkent Uzbekistan Computer Science and Engineering Francis Xavier Engineering College Tirunelveli India School of Information Technology Digital Media and Mass Communication Tmc Institute in Tashkent Tashkent Uzbekistan

ISBN: (纸本)9798350342338

Drowsy driving poses a major threat to road safety, demanding effective solutions. To address this issue, this paper introduces a cutting-edge drowsy driving detection system that leverages advanced face detection algorithms to analyze real-time facial features. By closely monitoring eye movements and mouth patterns, the system is capable of accurately identifying early signs of fatigue. In response, timely warnings are promptly issued to the driver through various means, including audible alarms, visual cues, or even physical interventions. Although the system holds great potential in enhancing road safety, there are still challenges to overcome, particularly regarding accuracy and adaptability. However, ongoing research endeavors aim to refine the algorithms and integrate additional physiological and behavioral measures, with the goal of further improving the system's capabilities. By preventing accidents caused by drowsiness, this innovative system demonstrates a promising pathway towards bolstering road safety. By addressing the critical issue of drowsy driving, it contributes significantly to the overall safety of drivers and passengers alike, making our roads a safer place for everyone. © 2023 IEEE.

关键词： allowed distraction time braking distance deep learning driver's fatigue level Drowsy driving face analysis reaction distance

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Fast Omni-Directional Image Super-Resolution: Adapting the Implicit Image Function with Pixel and Semantic-Wise Spherical Geometric Priors

arXiv

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arXiv 2025年

作者： Shen, Xuelin Wang, Yitong Zheng, Silin Xiao, Kang Yang, Wenhan Wang, Xu College of Computer Science and Software Engineering Shenzhen University China China Peng Cheng Laboratory China

In the context of Omni-Directional Image (ODI) Super-Resolution (SR), the unique challenge arises from the nonuniform oversampling characteristics caused by EquiRectangular Projection (ERP). Considerable efforts in designing complex spherical convolutions or polyhedron reprojection offer significant performance improvements but at the expense of cumbersome processing procedures and slower inference speeds. Under these circumstances, this paper proposes a new ODI-SR model characterized by its capacity to perform Fast and Arbitrary-scale ODI-SR processes, denoted as FAOR. The key innovation lies in adapting the implicit image function from the planar image domain to the ERP image domain by incorporating spherical geometric priors at both the latent representation and image reconstruction stages, in a low-overhead manner. Specifically, at the latent representation stage, we adopt a pair of pixel-wise and semantic-wise sphere-to-planar distortion maps to perform affine transformations on the latent representation, thereby incorporating it with spherical properties. Moreover, during the image reconstruction stage, we introduce a geodesic-based resampling strategy, aligning the implicit image function with spherical geometrics without introducing additional parameters. As a result, the proposed FAOR outperforms the state-of-the-art ODI-SR models with a much faster inference speed. Extensive experimental results and ablation studies have demonstrated the effectiveness of our design. Copyright © 2025, The Authors. All rights reserved.

关键词： Spheres

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Mutation Testing Techniques for Android Applications: A Comparative Study 2

Mutation Testing Techniques for Android Applications: A Comp...

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2nd International Conference on Multidisciplinary engineering and Applied science, ICMEAS 2023

作者： Salihu, Ibrahim Anka Usman, Asma'U Eke, Ndukwe-Oke Ibrahim, Rosziati Kusharki, Muhammad Bello Nile University of Nigeria Department Software Engineering Abuja Nigeria Abdu-Gusau Polytehcnic Department Computer Science Talata Mafara Nigeria Nile Univeristy of Nigeria Department of Computer Science Abuja Nigeria Universiti Tun Hussein Onn Malaysia Department Software Engineering Batu Pahat Malaysia National Defence College Department of Information and Communication Technology Abuja Nigeria

ISBN: (纸本)9798350358834

Mobile applications are becoming increasingly used to achieve various computing needs. Hence, it is essential to guarantee quality of the applications. software testing has been the main activity for ensuring the quality of software, however, it is a critical and costly activity. Furthermore, code coverage is commonly used as metric for verifying the effectiveness of testing technique. However, numerous researchers and experts claimed that considering code coverage only to ascertain the testing quality is not enough to ensure quality of apps. In view of this, mutation testing has been introduced to complement the procedure by assuring that apps behave as expected and are released free of faults. Several techniques/tools were proposed based on different syntax and mutation operators. This study presents a comparative study of six (6) mutation techniques/tools for Android applications to highlight their strengths and weakness which will give an insight to researchers on the directions for further research. © 2023 IEEE.

关键词： software testing

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A Novel Subspace-Based GMM Clustering Ensemble Algorithm for High-Dimensional Data

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Chinese Journal of Electronics 2025年第2期34卷 612-629页

作者： He Yulin He Yingting Zhan Zhaowu Philippe Fournier-Viger Huang Joshua Zhexue Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ) Shenzhen 518107 China College of Computer Science and Software Engineering Shenzhen University Shenzhen 518060 China China GridCom Co. Ltd. Shenzhen 518109 China

The Gaussian mixture model (GMM) is a classical probabilistic representation model widely used in unsupervised learning. GMM performs poorly on high-dimensional data (HDD) due to the requirement of estimating a large number of parameters with relatively few observations. To address this, the paper proposes a novel subspace-based GMM clustering ensemble (SubGMM-CE) algorithm tailored for HDD. The proposed SubGMM-CE algorithm comprises three key components. A series of low-dimensional subspaces are dynamically determined, considering the optimal number of GMM components. The GMM-based clustering algorithm is applied to each subspace to obtain a series of heterogeneous GMM models. These GMM base clustering results are merged using the newly-designed relabeling strategy based on the average shared affiliation probability, generating the final clustering result for high-dimensional unlabeled data. An exhaustive experimental evaluation validates the feasibility, rationality, effectiveness, and robustness to noise of the SubGMM-CE algorithm. Results show that SubGMM-CE achieves higher stability and more accurate clustering results, outperforming nine state-of-the-art clustering algorithms in normalized mutual information, clustering accuracy, and adjusted rand index scores. This demonstrates the viability of the SubGMM-CE algorithm in addressing HDD clustering challenges.

关键词： Gaussian mixture model (GMM) High-dimensional clustering Subspace learning Clustering ensemble Uncertainty-based relabeling

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Deep Hierarchies and Invariant Disease-Indicative Feature Learning for computer Aided Diagnosis of Multiple Fundus Diseases 39

Deep Hierarchies and Invariant Disease-Indicative Feature Le...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Lin, Yuxin Wang, Wei Luo, Xiaoling Wu, Zhihao Liu, Chengliang Wen, Jie Xu, Yong School of Computer Science and Technology Harbin Institute of Technology Shenzhen China College of Computer Science and Software Engineering Shenzhen University China Computer Science and Engineering Hong Kong University of Science and Technology Hong Kong

ISBN: (纸本)157735897X

With the advancement of computer vision, numerous models have been proposed for screening of fundus diseases. However, the recognition of multiple fundus diseases is often hampered by the simultaneous presence of multiple disease types and the confluence of lesion types in fundus images. This paper addresses these challenges by conceptualizing them as multi-level feature fusion and self-supervised disease-indicative feature learning problems. We decode fundus images at various levels of granularity to delineate scenarios wherein multiple diseases and lesions co-occur. To effectively integrate these features, we introduce a hierarchical vision transformer (HVT) that adeptly captures both inter-level and intra-level dependencies. A novel forward-attention module is proposed to enhance the integration of lower-level semantic information into higher semantic layers, thereby enriching the representation of complex features. Additionally, we introduce a novel self-supervised mask-consistent feature learner (MCFL). Unlike traditional mask-autoencoders that reconstruct original images using encoder-decoder structures, MCFL utilizes a teacher-student framework to reconstruct mask-consistent feature maps. In this setup, exponential moving averaging is employed to derive classification-guided features, serving as labels for reconstruction rather than merely reconstructing the original images. This innovative approach facilitates the extraction of disease-indicative features. Extensive experiments demonstrate that our method significantly outperforms existing state-of-the-art models. © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Self-supervised learning

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A Framework Design of ML Classifier Algorithm for Retrieve the Information about the Drugs and its Quality 4

A Framework Design of ML Classifier Algorithm for Retrieve t...

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4th IEEE International Conference on Advance Computing and Innovative Technologies in engineering, ICACITE 2024

作者： Meenakshi, M. Ruban, S. Nandhini, T.J. Loganayagi, S. Sayed, Raad Muhammed Hassan, Enas Al-Saidi, Imad Al-Din Hussein Saveetha University Saveetha School of Engineering Chennai Thandalam602117 India St Aloysius Deemed to Be University Department of Software Technology India Saveetha University Saveetha School of Engineering-SIMATS Department of Computer Science and Engineering chennai India Department of Computer Science and Engineering chennai tamil nadu thandalam602105 India Collage of Nursing National University of Science and Tqechnology dhi qar64001 Iraq Al-Farahidi University Baghdad Iraq Al-kunooze University College Computer Engineer Department basra Iraq

ISBN: (纸本)9798350360165

Every day, pharmaceutical firms have to do extensive product analyses. The same product is frequently registered more than once in several systems, each using unique properties. For these businesses, precise and high-quality information is crucial, especially considering the nature of the pharmaceuticals these goods contain. The hypothesis of this research project is that machine learning provides a workable way to effectively combine different data sources and match records pertaining to the same product. It is no longer feasible to match records by hand because of the extraordinarily large volume that has to be processed. In a large data context, this paper presents a framework for matching pharmacological records using machine learning approaches. The framework trains machine learning by utilising specified criteria for record matching. These models are then tested by forecasting matches between records that deviate from these preset guidelines. Lastly, by creating a huge variety of record combinations and forecasting matches based on them, the system mimics the production environment. The findings show that although the training datasets produce good results - the top model has an average accuracy of about 85% - the production environment has its own set of difficulties. In spite of this, matches that depart from established norms are effectively predicted by the framework. These results highlight how machine learning can be used to perform record matching tasks, as manual processing at this size is impossible. © 2024 IEEE.

关键词： Adversarial machine learning

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The Role of Depth, Width, and Tree Size in Expressiveness of Deep Forest 27

The Role of Depth, Width, and Tree Size in Expressiveness of...

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27th European Conference on Artificial Intelligence, ECAI 2024

作者： Lyu, Shen-Huan Wu, Jin-Hui Zheng, Qin-Cheng Ye, Baoliu Key Laboratory of Water Big Data Technology of Ministry of Water Resources Hohai University China College of Computer Science and Software Engineering Hohai University China National Key Laboratory for Novel Software Technology Nanjing University China School of Artificial Intelligence Nanjing University China

ISBN: (纸本)9781643685489

Random forests are classical ensemble algorithms that construct multiple randomized decision trees and aggregate their predictions using naive averaging. Zhou and Feng [51] further propose a deep forest algorithm with multi-layer forests, which outperforms random forests in various tasks. The performance of deep forests is related to three hyperparameters in practice: depth, width, and tree size, but little has been known about its theoretical explanation. This work provides the first upper and lower bounds on the approximation complexity of deep forests concerning the three hyperparameters. Our results confirm the distinctive role of depth, which can exponentially enhance the expressiveness of deep forests compared with width and tree size. Experiments validate these theoretical findings. The detailed proof and code are available in the full version [31]. © 2024 The Authors.

关键词： Random forests

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