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AE-TPGG:a novel autoencoder-based approach for single-cell RNA-seq data imputation and dimensionality reduction

Frontiers of computer science 2023年第3期17卷 217-234页

作者： Shuchang ZHAO Li ZHANG Xuejun LIU MIIT Key Laboratory of Pattern Analysis and Machine Intelligence College of Computer Science and TechnologyNanjing University of Aeronautics and AstronauticsNanjing 211106China Collaborative Innovation Center of Novel Software Technology and Industrialization Nanjing 210023China College of Computer Science and Technology Nanjing Forestry UniversityNanjing 210037China

Single-cell RNA sequencing(scRNA-seq)technology has become an effective tool for high-throughout transcriptomic study,which circumvents the averaging artifacts corresponding to bulk RNA-seq technology,yielding new perspectives on the cellular diversity of potential superficially homogeneous *** various sequencing techniques have decreased the amplification bias and improved capture efficiency caused by the low amount of starting material,the technical noise and biological variation are inevitably introduced into experimental process,resulting in high dropout events,which greatly hinder the downstream *** the bimodal expression pattern and the right-skewed characteristic existed in normalized scRNA-seq data,we propose a customized autoencoder based on a twopart-generalized-gamma distribution(AE-TPGG)for scRNAseq data analysis,which takes mixed discrete-continuous random variables of scRNA-seq data into account using a twopart model and utilizes the generalized gamma(GG)distribution,for fitting the positive and right-skewed continuous *** adopted autoencoder enables AE-TPGG to captures the inherent relationship between *** addition to the ability of achieving low-dimensional representation,the AETPGG model also provides a denoised imputation according to statistical characteristic of gene *** on real datasets demonstrate that our proposed model is competitive to current imputation methods and ameliorates a diverse set of typical scRNA-seq data analyses.

关键词： scRNA-seq autoencoder TPGG data imputation dimensionality reduction

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Deep Learning Based Side-Channel Attack Detection for Mobile Devices Security in 5G Networks

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清华大学学报自然科学版（英文版） 2025年第3期30卷 1012-1026页

作者： Amjed A.Ahmed Mohammad Kamrul Hasan Ali Alqahtani Shayla Islam Bishwajeet Pandey Leila Rzayeva Huda Saleh Abbas Azana Hafizah Mohd Aman Nayef Alqahtani Center for Cyber Security Faculty of Information Science and TechnologyUniversiti Kebangsaan MalaysiaBangi 43600Malaysia Imam Alkadhim University College Department of Computer Techniques EngineeringBaghdad 10066Iraq Center for Cyber Security Faculty of Information Science and TechnologyUniversiti Kebangsaan MalaysiaBangi 43600Malaysia Department of Networks and Communications Engineering College of Computer Science and Information SystemsNajran UniversityNajran 61441Saudi Arabia Institute of Computer Science and Digital Innovation UCSI UniversityKuala Lumpur 56000Malaysia Department of Intelligent Systems and Cyber Security Astana IT UniversityAstana 20000Kazakstan Department of Computer Science College of Computer Science and EngineeringTaibah UniversityMadinah 42353Saudi Arabia Department of Electrical Engineering College of EngineeringKing Faisal UniversityAl-Hofuf 31982Saudi Arabia

Mobile devices within Fifth Generation(5G)networks,typically equipped with Android systems,serve as a bridge to connect digital gadgets such as global positioning system,mobile devices,and wireless routers,which are vital in facilitating end-user communication ***,the security of Android systems has been challenged by the sensitive data involved,leading to vulnerabilities in mobile devices used in 5G *** vulnerabilities expose mobile devices to cyber-attacks,primarily resulting from security ***-permission apps in Android can exploit these channels to access sensitive information,including user identities,login credentials,and geolocation *** such attack leverages"zero-permission"sensors like accelerometers and gyroscopes,enabling attackers to gather information about the smartphone's *** underscores the importance of fortifying mobile devices against potential future *** research focuses on a new recurrent neural network prediction model,which has proved highly effective for detecting side-channel attacks in mobile devices in 5G *** conducted state-of-the-art comparative studies to validate our experimental *** results demonstrate that even a small amount of training data can accurately recognize 37.5％of previously unseen user-typed ***,our tap detection mechanism achieves a 92％accuracy rate,a crucial factor for text *** findings have significant practical implications,as they reinforce mobile device security in 5G networks,enhancing user privacy,and data protection.

关键词： Fifth Generation(5G)networks smartphone information leakage Side-Channel Attack(SCA) deep learning

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Intelligent field monitoring system for cruciferous vegetable pests using yellow sticky board images and an improved Cascade R-CNN

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Journal of Integrative Agriculture 2025年第1期24卷 220-234页

作者： Yufan Gao Fei Yin Chen Hong Xiangfu Chen Hang Deng Yongjian Liu Zhenyu Li Qing Yao School of Computer Science and Technology Zhejiang Sci-Tech UniversityHangzhou 310018China Collaborative Innovation Center Guangdong Academy of Agricultural SciencesGuangzhou 510640China

Cruciferous vegetables are important edible vegetable ***,they are susceptible to various pests during their growth process,which requires real-time and accurate monitoring of these pests for pest forecasting and scientific *** yellow sticky boards is a common way to monitor and trap those pests which are attracted to the yellow *** achieve real-time,low-cost,intelligent monitoring of these vegetable pests on the boards,we established an intelligent monitoring system consisting of a smart camera,a web platform and a pest detection algorithm deployed on a *** the operator sets the monitoring preset points and shooting time of the camera on the system platform,the camera in the field can automatically collect images of multiple yellow sticky boards at fixed places and times every *** pests trapped on the yellow sticky boards in vegetable fields,Plutella xylostella,Phyllotreta striolata and flies,are very small and susceptible to deterioration and breakage,which increases the difficulty of model *** solve the problem of poor recognition due to the small size and breaking of the pest bodies,we propose an intelligent pest detection algorithm based on an improved Cascade R-CNN model for three important cruciferous crop *** algorithm uses an overlapping sliding window method,an improved Res2Net network as the backbone network,and a recursive feature pyramid network as the neck *** results of field tests show that the algorithm achieves good detection results for the three target pests on the yellow sticky board images,with precision levels of 96.5,92.2 and 75.0%,and recall levels of 96.6,93.1 and 74.7%,respectively,and an F_(1) value of *** with other algorithms,our algorithm has a significant advantage in its ability to detect small target *** accurately obtain the data for the newly added pests each day,a two-stage pest matching algorithm was *** algorithm performed well and achieved

关键词： vegetable pests yellow sticky boards intelligent monitoring system deep learning pest detection

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Media Power Measuring via Emotional Contagion

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Journal of Social Computing 2024年第1期5卷 15-35页

作者： Xue Lin Hong Huang Zongya Li Hai Jin National Engineering Research Center for Big Data Technology and System Services Computing Technology and System LabCluster and Grid Computing LabSchool of Computer Science and TechnologyHuazhong University of Science and TechnologyWuhan 430074China School of Journalism Huazhong University of Science and TechnologyWuhan 430074China

Media power,the impact that media have on public opinion and perspectives,plays a significant role in maintaining internal stability,exerting external influence,and shaping international dynamics for nations/***,prior research has primarily concentrated on news content and reporting time,resulting in limitations in evaluating media *** more accurately assess media power,we use news content,news reporting time,and news emotion simultaneously to explore the emotional contagion between *** use emotional contagion to measure the mutual influence between media and regard the media with greater impact as having stronger media *** propose a framework called Measuring Media Power via Emotional Contagion(MMPEC)to capture emotional contagion among media,enabling a more accurate assessment of media power at the media and national/regional *** also interprets experimental results through correlation and causality analyses,ensuring *** analyses confirm the higher accuracy of MMPEC compared to other baseline models,as demonstrated in the context of COVID-19-related news,yielding compelling and interesting insights.

关键词： explainability media power emotional contagion

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Improved Manta Ray Foraging Optimizer-based SVM for Feature Selection Problems:A Medical Case Study

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Journal of Bionic Engineering 2024年第1期21卷 409-425页

作者： Adel Got Djaafar Zouache Abdelouahab Moussaoui Laith Abualigah Ahmed Alsayat Faculty of informatics University of Science and Technology Houari BoumedieneAlgiersAlgeria LRIA Laboratory University of Science and Technology Houari BoumedieneAlgiersAlgeria Computer Science Department University of Mohamed El Bachir El IbrahimiBordj Bou ArreridjAlgeria Computer Science Department University of Ferhat AbbasSetifAlgeria Computer Science Department Prince Hussein Bin Abdullah Faculty for Information TechnologyAl al-Bayt UniversityMafraq 25113Jordan Department of Computer Science College of Computer and Information SciencesJouf UniversityJoufSaudi Arabia Department of Electrical and Computer Engineering Lebanese American University13-5053ByblosLebanon Hourani Center for Applied Scientific Research Al-Ahliyya Amman UniversityAmman 19328Jordan MEU Research Unit Middle East UniversityAmman 11831Jordan Applied science research center Applied science private universityAmman 11931Jordan School of Computer Sciences Universiti Sains Malaysia11800 PenangMalaysia School of Engineering and Technology Sunway University Malaysia27500 Petaling JayaMalaysia

Support Vector Machine(SVM)has become one of the traditional machine learning algorithms the most used in prediction and classification ***,its behavior strongly depends on some parameters,making tuning these parameters a sensitive step to maintain a good *** the other hand,and as any other classifier,the performance of SVM is also affected by the input set of features used to build the learning model,which makes the selection of relevant features an important task not only to preserve a good classification accuracy but also to reduce the dimensionality of *** this paper,the MRFO+SVM algorithm is introduced by investigating the recent manta ray foraging optimizer to fine-tune the SVM parameters and identify the optimal feature subset *** proposed approach is validated and compared with four SVM-based algorithms over eight benchmarking ***,it is applied to a disease Covid-19 *** experimental results show the high ability of the proposed algorithm to find the appropriate SVM’s parameters,and its acceptable performance to deal with feature selection problem.

关键词： Support vector machine Parameters tuning Feature selection Bioinspired algorithms Manta ray foraging optimizer

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UAV Localization with Unreliable Observations in Hostile Underground Environments

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Journal of computer science & technology 2024年第6期39卷 1401-1418页

作者： Peng-Peng Chen Kui-Yuan Zhang Shou-Wan Gao Si-Yi Ren School of Computer Science and Technology China University of Mining and TechnologyXuzhou 221116China Mine Digitization Engineering Research Center Ministry of EducationXuzhou 221116China Shenzhen Research Institute China University of Mining and TechnologyShenzhen 518057China

The accurate and robust unmanned aerial vehicle(UAV)localization is significant due to the requirements of safety-critical monitoring and emergency wireless communication in hostile underground *** range-based localization approaches fundamentally rely on the assumption that the environment is relatively ideal,which enables a precise range for ***,radio propagation in the underground environments may be dramatically influenced by various equipments,obstacles,and ambient *** this case,inaccurate range measurements and intermittent ranging failures inevitably occur,which leads to severe localization performance *** address the challenges,a novel UAV localization scheme is proposed in this paper,which can effectively handle unreliable observations in hostile underground *** first propose an adaptive extended Kalman filter(EKF)based on the fusion of ultra-wideband(UWB)and inertial measurement unit(IMU)to detect and adjust the inaccurate range *** to deal with intermittent ranging failures,we further design the constraint condition by limiting the system ***,the auto-regressive model is proposed to implement the localization in the ranging blind areas by reconstructing the lost ***,extensive simulations have been conducted to verify the *** carry out field experiments in an underground garage and a coal mine based on P440 UWB *** show that the localization accuracy is improved by 16.9%compared with the recent methods in the hostile underground environments.

关键词： unmanned aerial vehicle(UAV)localization Kalman filter ultra-wideband(UWB)observation hostile underground environment

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Short-Term Rolling Prediction of Tropical Cyclone Intensity Based on Multi-Task Learning with Fusion of Deviation-Angle Variance and Satellite Imagery

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Advances in Atmospheric sciences 2025年第1期42卷 111-128页

作者： Wei TIAN Ping SONG Yuanyuan CHEN Yonghong ZHANG Liguang WU Haikun ZHAO Kenny Thiam Choy LIM KAM SIAN Chunyi XIANG School of Software Nanjing University of Information Science and TechnologyNanjing 210044China School of Computer Nanjing University of Information Science and TechnologyNanjing 210044China School of Automation Nanjing University of Information Science and TechnologyNanjing 210044China Fudan University Shanghai 200433China Pacific Typhoon Research Center Nanjing University of Information Science and TechnologyNanjing 210044China Wuxi University Wuxi 214100China National Meteorological Center Beijing 100081China

Tropical cyclones(TCs)are one of the most serious types of natural disasters,and accurate TC activity predictions are key to disaster prevention and ***,TC track predictions have made significant progress,but the ability to predict their intensity is obviously lagging *** present,research on TC intensity prediction takes atmospheric reanalysis data as the research object and mines the relationship between TC-related environmental factors and intensity through deep ***,reanalysis data are non-real-time in nature,which does not meet the requirements for operational forecasting ***,a TC intensity prediction model named TC-Rolling is proposed,which can simultaneously extract the degree of symmetry for strong TC convective cloud and convection intensity,and fuse the deviation-angle variance with satellite images to construct the correlation between TC convection structure and *** TCs'complex dynamic processes,a convolutional neural network(CNN)is used to learn their temporal and spatial *** real-time intensity estimation,multi-task learning acts as an implicit time-series *** model is designed with a rolling strategy that aims to moderate the long-term dependent decay problem and improve accuracy for short-term intensity *** multiple tasks are correlated,the loss function of 12 h and 24 h are *** testing on a sample of TCs in the Northwest Pacific,with a 4.48 kt root-mean-square error(RMSE)of 6 h intensity prediction,5.78 kt for 12 h,and 13.94 kt for 24 h,TC records from official agencies are used to assess the validity of TC-Rolling.

关键词： tropical cyclone intensity structure rolling prediction multi-task

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Adversarial Geometric Attacks for 3D Point Cloud Object Tracking

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IEEE Transactions on Multimedia 2025年 27卷 3144-3157页

作者： Yao, Rui Zhang, Anqi Zhou, Yong Zhao, Jiaqi Liu, Bing ElSaddik, Abdulmotaleb China University of Mining and Technology School of Computer Science and Technology Mine Digitization Engineering Research Center Ministry of Education Xuzhou221116 China University of Ottawa School of Electrical Engineering and Computer Science OttawaONK1N 6N5 Canada

3D point cloud object tracking (3D PCOT) plays a vital role in applications such as autonomous driving and robotics. Adversarial attacks offer a promising approach to enhance the robustness and security of tracking models. However, existing adversarial attack methods for 3D PCOT seldom leverage the geometric structure of point clouds and often overlook the transferability of attack strategies. To address these limitations, this paper proposes an adversarial geometric attack method tailored for 3D PCOT, which includes a point perturbation attack module (non-isometric transformation) and a rotation attack module (isometric transformation). First, we introduce a curvature-aware point perturbation attack module that enhances local transformations by applying normal perturbations to critical points identified through geometric features such as curvature and entropy. Second, we design a Thompson sampling-based rotation attack module that applies subtle global rotations to the point cloud, introducing tracking errors while maintaining imperceptibility. Additionally, we design a fused loss function to iteratively optimize the point cloud within the search region, generating adversarially perturbed samples. The proposed method is evaluated on multiple 3D PCOT models and validated through black-box tracking experiments on benchmarks. For P2B, white-box attacks on KITTI reduce the success rate from 53.3% to 29.6% and precision from 68.4% to 37.1%. On NuScenes, the success rate drops from 39.0% to 27.6%, and precision from 39.9 to 26.8%. Black-box attacks show a transferability, with BAT showing a maximum 47.0% drop in success rate and 47.2% in precision on KITTI, and a maximum 22.5% and 27.0% on NuScenes. © 1999-2012 IEEE.

关键词： Iterative methods

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Enhanced Federated Learning Framework based on Deep Learning and Neutrosophic Set for Android Malware Classification

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Neutrosophic Sets and Systems 2025年 82卷 781-799页

作者： Abdellah, Mohamed Refaat Oudah, Hasan H. Badawy, Ahmed Mohamed Ahmed Mohamed, Abdelfattah Abdelfattah M. Bedier, Shady Ahmed Metwaly, Ahmed A. Eassa, Mohamed Abdelhafeez, Ahmed The Department of Computer Science College of Information Technology Misr University for Science and Technology Cairo Egypt College of Arts Department of Journalism Ahl Al Bayt University Karbala Iraq Cybersecurity Department Faculty of Computers and Artificial Intelligence Helwan University Egypt Computer Science Department Faculty of Information System and Computer Science October 6 University Giza12585 Egypt Department of Computer Science Faculty of Computers and Informatics Zagazig University Zagazig44519 Egypt Applied Science Research Center Applied Science Private University Amman Jordan

Malware detection is one of the critical tasks of cybersecurity, especially considering the growing popularity of mobile devices. The integrity and security of mobile ecosystems rely on the capacity to identify malware quickly and precisely, as security hacks against these devices become increasingly frequent. Due to the requirement for massive volumes of data aggregation, traditional centralized machine learning (ML) techniques for malware detection face challenges with data sharing, computational complexity, and privacy. This research addresses these challenges by proposing a novel model, called "CNN-Fed", which is based on Convolutional Neural Networks (CNN) with Federated Learning (Fed). The main goal of this work is to create a global classifier for Android malware detection that is highly accurate and does not require centralised data aggregation. Using four benchmark datasets—Drebin, Malgenome, Kronodroid, and Tuandromd—the CNN-Fed model is trained across many clients in a federated setting under the suggested architecture. Then we use multi-criteria decision-making (MCDM) methodology to evaluate the number of clients and number of rounds. We use two MCDM methods such as Entropy methodology to compute the criteria weights and the CoCoSo methodology to rank the alternatives. We use the interval-valued neutrosophic sets (IVNSs) to deal with uncertainty and vague information. We use five criteria such as accuracy, F1-score, precision, recall, and FP and six of alternatives refer to the number of clients and number of rounds. The results show the best alternative has a larger number of clients and rounds. © 2025, University of New Mexico. All rights reserved.

关键词： Android malware

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Real-Time On-Site Ground-Motion Prediction Using ResNet

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Seismological Research Letters 2025年第3期96卷 1834-1845页

作者： Yang, Tzu-Yi Chen, Da-Yi Chin, Tai-Lin Mobile Computing and Networking Lab Department of Computer Science and Information Engineering National Taiwan University of Science and Technology Taipei Taiwan Seismological Center of Central Weather Administration Taipei Taiwan

On-site warnings can decrease the range of late alert zone during earthquakes. This study develops a deep learning model to predict whether the maximum peak ground acceleration at a station exceeds 25 Gal based on the waveform of the initial P-wave. A ResNet architecture model is developed to address the degradation problem in multilayered models, thereby enhancing performance. The model exhibits high performance for a test dataset, achieving F1-scores higher than 90% over various time windows, and outperforms the traditional progressive displacement value (Pdv) method and newer convolutional neural network (CNN) methods. Ground motion of two actual earthquakes are applied to test the model and validate its prediction capabilities, and the results indicate that the proposed model has higher accuracy and speed than Pdv and CNN methods. In addition, the model is integrated with the Earthworm earthquake early warning system for real-time waveform analysis and prediction, extending the model’s applicability beyond experimental stages to online systems. The proposed method is also tested using data from earthquakes that occurred in March 2023. © Seismological Society of America.

关键词： Convolutional neural networks

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