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IET International Radar Conference 2023, IRC 2023

作者： Chen, Hao Lei, Yi Key Lab of Aperture Array and Space Application East China Research Institute of Electronic Engineering Hefei China School of Computer Science and Information Engineering Hefei University of Technology Hefei China

ISBN: (纸本)9781839539954

Range accuracy and radial velocity accuracy are important indexes in pulsed radar. However, in pulsed radar using linear frequency modulation (LFM) pulse, the range-Doppler coupling of LFM signal will lead to large range error in high-speed target measurement. The target's radial velocity is computed using range differentiation in traditional method, which is also not accurate. This paper presents a measurement method based on up-chirp and down-chirp LFM signal to get the target's accurate range and radial velocity simultaneously. Experiments confirm that the proposed method outperforms traditional method in both range accuracy and radial velocity accuracy. © The Institution of Engineering & technology 2023.

关键词： Chirp modulation

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Sparse Attention-based Hierarchical Node Representation for Spatial Domain Identification

Sparse Attention-based Hierarchical Node Representation for ...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Peng, Wei Ping, Zhihao Dai, Wei Fu, Xiaodong Liu, Li Liu, Lijun Yu, Ning Kunming University of Science and Technology Faculty of Information Engineering and Automation Kunming China Computer Technology Application Key Lab of Yunnan Province Kunming University of Science and Technology Kunming China State University of New York The College at Brockport Department of Computing Sciences BrockportNY14422 United States

ISBN: (纸本)9798350386226

Using deep learning models on spatial transcriptomics data to identify the spatial domain is crucial for uncovering the spatial distribution of cells and gene expression patterns within tissues, essential for understanding complex biological processes and disease mechanisms. Existing methods for spatial domain partitioning often rely on predefined adjacency relationships at a single scale, overlooking the hierarchical structure and functional characteristics of biological tissues. In this paper, we propose SpaNFM, a novel method that leverages sparse attention-based hierarchical node representation and multi-view contrastive learning for spatial domain identification in spatial transcriptomics data. The SpaNFM first treats each spot as a node and constructs two views using different data augmentation techniques based on tissue image information, gene expression profiles, and spatial coordinates of cells. Subsequently, SpaNFM utilizes a sparse attention-based hierarchical node fusion module to generate coarse-grained node representations. This fine-to-coarse hierarchical structure integrates complementary information from multi-granularity node features and reduces model complexity due to the decreased node size. The model parameters are updated using gene expression reconstruction loss and contrastive loss on the coarse-grained node representations from the two views. Finally, the learned node features are subjected to downstream clustering using the Leiden algorithm. We tested SpaNFM on the human dorsolateral prefrontal cortex dataset. The results demonstrate that SpaNFM outperforms other state-of-the-art methods in most cases. The data and code are available at: https://***/weiba/SpaNFM © 2024 IEEE.

关键词： Contrastive Learning

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RailPC: A large-scale railway point cloud semantic segmentation dataset

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CAAI Transactions on Intelligence technology 2024年第6期9卷 1548-1560页

作者： Tengping Jiang Shiwei Li Qinyu Zhang Guangshuai Wang Zequn Zhang Fankun Zeng Peng An Xin Jin Shan Liu Yongjun Wang Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application Nanjing Normal UniversityNanjingChina Technology Innovation Center for Integrated Applications in Remote Sensing and Navigation Ministry of Natural ResourcesNanjingChina Eastern Institute of Technology(EIT) NingboChina Tianjin Key Laboratory of Rail Transit Navigation Positioning and Spatio-temporal Big Data Technology TianjinChina College of Computer Science and Engineering Northwest Normal UniversityLanzhouChina Mckelvey School of Engineering Washington University in St.LouisSt.LouisMissouriUSA School of Electronic and Information Engineering Ningbo University of TechnologyNingboChina

Semantic segmentation in the context of 3D point clouds for the railway environment holds a significant economic value,but its development is severely hindered by the lack of suitable and specific ***,the models trained on existing urban road point cloud datasets demonstrate poor generalisation on railway data due to a large domain gap caused by non-overlapping special/rare categories,for example,rail track,track bed *** harness the potential of supervised learning methods in the domain of 3D railway semantic segmentation,we introduce RailPC,a new point cloud *** provides a large-scale dataset with rich annotations for semantic segmentation in the railway ***,RailPC contains twice the number of annotated points compared to the largest available mobile laser scanning(MLS)point cloud dataset and is the first railway-specific 3D dataset for semantic *** covers a total of nearly 25 km railway in two different scenes(urban and mountain),with 3 billion points that are finely labelled as 16 most typical classes with respect to railway,and the data acquisition process is completed in China by MLS *** extensive experimentation,we evaluate the performance of advanced scene understanding methods on the annotated dataset and present a synthetic analysis of semantic segmentation *** on our findings,we establish some critical challenges towards railway-scale point cloud semantic *** dataset is available at https://***/NNU-GISA/GISA-RailPC,and we will continuously update it based on community feedback.

关键词： data benchmark MLS point clouds railway scene semantic segmentation

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A Robust DL Approach for Detection of Invasive Ductal Carcinoma in Whole Slide Images using DenseNet169

A Robust DL Approach for Detection of Invasive Ductal Carcin...

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2024 International Conference on Communication, computer Sciences and Engineering, IC3SE 2024

作者： Menaka, C. Sharma, Sunil Naval, Preeti Brar, Khushmeen Anand, Rohit Alabdeli, Haider Pant, Shivani Department Of Computer Science And Information Technology Bangalore India Vivekananda Global University Department Of Computer Science & Application Jaipur India Maharishi University Of Information Technology Maharishi School Of Engineering & Technology Uttar Pradesh India Chandigarh University Department Of Electronics And Communication Engineering Punjab Mohali India G.B Pant Okhla-1 Campus Department Of Ece New Delhi India The Islamic University Najaf Iraq Uttaranchal University Uttaranchal Institute Of Technology Uttarakhand India

ISBN: (纸本)9798350366846

The present research introduces a deep learning method for automatically recognizing and evaluating tissue areas that correspond to invasive ductal carcinoma (IDC) in whole slide images (WSI) of breast cancer (BCa). Learn-from-data techniques which utilize computational simulations of the learning process are called deep learning approaches. It has been demonstrated that these methodologies outperform conventional methods in resolving the challenging issues in a number of domains, particularly object and speech recognition. The main reason why making an invasive breast cancer evaluation is an exhausting and laborious process is that, it necessitates a pathologist to scan a huge number of benign regions with the aim to determine the spots that are malignant. Effectively characterizing IDC in WSI is a prerequisite for evaluating the severity of tumors and predicting patient prognosis. In order to support diagnosis, the DL approach in this study draws out many convolutional neural networks (CNNs) for perceptible semantic examination of tumor locations. A WSI database of 162 patients with IDC diagnoses is exploited to assess the technique, and 170 slides were chosen for training. The objective of the experimental analysis was to assess the accuracy of the classifier in identifying IDC tissue areas in WSI. A fusion of optimization techniques namely Best First and Evolutionary Search has been employed for feature reduction and improvement. When using DenseNet169, our approach produced the highest quantitative outcomes for the computerized identification of IDC sections in WSI with regard to accuracy 87.46% in comparison with an approach using EfficientNetB0 and ML classifiers for invasive tumor categorization employing Random Forest, BayesNet, SVM, Naïve Bayes and Decision Tree with accuracy of 80.65%. © 2024 IEEE.

关键词： Diagnosis

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Network Security and Protection Strategies for Big Data: Challenges and Innovations 6

Network Security and Protection Strategies for Big Data: Cha...

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6th International Conference on Contemporary Computing and Informatics, IC3I 2023

作者： Gupta, Manish Kumar Rai, Ashok Kumar Farooq, Mansoor Santhiya, P. Madan Mohan Malaviya University of Technology Department of Information Technology & Computer Application UP Gorakhpur India Madan Mohan Malaviya University of Technology Department of Computer Science & Engineering UP Gorakhpur India University of Kashmir Department of Management Studies Jammu & Kashmir India Sathyabama Institute of Science and Technology Department of Computer Science & Engineering Tamilnadu Chennai India

ISBN: (纸本)9798350304480

The emergence of Big Data (BD) has enabled organizations to process large and intricate datasets, uncover hidden patterns, gain valuable insights, and facilitate data sharing across networks. However, the transmission of data within organizational networks often involves sensitive information that necessitates an efficient and secure platform. Consequently, network security has become a paramount concern in the era of big data. Existing network security platforms must contend with vast and complex information to effectively predict and prevent potential real-time attacks. Nevertheless, these platforms typically rely on conventional approaches, rendering them inadequate for safeguarding big data. This paper focuses primarily on network security and protection strategies for big data. Firstly, we discuss the factors that impact network security platforms in the era of big data. Subsequently, we explore various strategies for ensuring security across networks, while also surveying recent research in the field. © 2023 IEEE.

关键词： Big data Datasets Network Privacy Security

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Design and Implementation of a Game Session Plugin based on Unreal Engine 23

Design and Implementation of a Game Session Plugin based on ...

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8th International Conference on Cyber Security and information Engineering, ICCSIE 2023

作者： Wen, Siyao Li, Dandan Yuan, Junlong Huang, Jingbin Zhou, Long School of Computer and Information Engineering Hubei Normal University Huangshi China National Engineering Research Center of Educational Big Data Application Technology Central China Normal University Wuhan China

ISBN: (纸本)9798400708800

With the rise in popularity of multiplayer online games, efficient game network communication has become increasingly vital. This research paper presents a comprehensive design and implementation of a network session plug-in, focusing on framework construction, data module integration, intermediate module development, and plug-in functionality testing. The primary objective of the plug-in is to enhance the convenience and efficiency of game network communication. Extensive testing, including performance evaluation, stress testing, and functional testing, has been conducted to validate the effectiveness and feasibility of the plug-in. The results showcase the successful development of a robust network session plug-in for multiplayer online games, offering valuable insights for future plug-in advancements. This research contributes significantly to the enhancement of game network technology, ultimately improving the overall player experience in multiplayer online gaming environments. © 2023 ACM.

关键词： Integration testing

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RTAD-HIS: Regulated transformer architecture based anomaly detection framework towards security in healthcare IoT systems

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Applied Soft Computing 2025年 177卷

作者： Rai, Arun Kumar Verma, Deepak Kumar Dwivedi, Rajendra Kumar Department of Computer Science and Engineering University Institute of Engineering & Technology Chhatrapati Shahu Ji Maharaj University Kanpur India Department of Information Technology and Computer Application Madan Mohan Malaviya University of Technology Gorakhpur India

The rapid expansion of Healthcare Internet of Things (HIS) devices has transformed modern medical systems by enabling real-time patient monitoring, seamless data exchange, and automated diagnostics. However, existing methods struggle with accurate anomaly detection, often facing overfitting and false positives/negatives due to vast and complex datasets. Reliable solutions are essential to ensure precise and timely anomaly identification of security threats and system vulnerabilities. This paper introduces the Regulated Transformer-Based Anomaly Detection (RTAD) model, which employs a Self-Regulated Transformer-Convolutional Residual Neural Network (SRTrans-ConvRNN) for anomaly detection in HIS data streams. The RTAD model utilizes temporal pooling layers to enable feature extraction, Stateful Long Short-Term Memory (SLSTM) layers to enable sequential learning, Multi-Head Self-Attention (MHSA) to enable parallel processing, and position-wise feed-forward neural networks to boost data processing and feature extraction. Additionally, an Anomaly Scoring Module (ASM) evaluates and ranks deviations from normal behavior patterns, thus ensuring efficient and effective anomaly detection. Experimental findings show that the RTAD realizes precision of 98.8 %, recall of 99.2 %, F1-score of 99.3 %, and overall accuracy of 99.6 % tested on three publicly available datasets such as Edith Cowan University - Internet of Health Things (ECU-IoHT) dataset, the Washington University in St. Louis - Enhanced Healthcare Monitoring System 2020 (WUSTL-EHMS 2020), and the Canadian Institute for Cybersecurity Intrusion Detection System 2017 (CICIDS2017) dataset. From these fact, it is a reliable real-time solution for anomaly detection in HIS environments. © 2025 Elsevier B.V.

关键词： Electrotherapeutics

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An Integrated Approach of Proposed Pruning Based Feature Selection Technique (PBFST) for Phishing E-mail Detection

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Recent Advances in computer Science and Communications 2022年第5期15卷 683-692页

作者： Hota, Hari Shanker Sharma, Dinesh Shrivas, Akhilesh Department of Computer Science & Application Atal Bihari Vajpayee Vishwavidyalaya CG Bilaspur India Department of Business Management & Accounting University of Maryland Eastern Shore United States Department of Computer Science & Information Technology Guru Ghasidas Vishwavidyalaya CG Bilaspur India

Background: The entire world is shifting towards electronic communication through Email for fast and secure communication. Millions of people, including organization, government, and others, are using Email services. This growing number of Email users are facing problems;therefore, detecting phishing Email is a challenging task, especially for non-IT users. Automatic detection of phishing Email is essential to deploy along with Email software. Various authors have worked in the field of phishing Email classification with different feature selection and optimization techniques for better performance. Objectives: This paper attempts to build a model for the detection of phishing Email using data mining techniques. This paper's significant contribution is to develop and apply Feature Selection Technique (FST) to reduce features from the phishing Email benchmark data set. Methods: The proposed Pruning Based Feature Selection Technique (PBFST) is used to determine the rank of feature based on the level of the tree where feature exists. The proposed algorithm is integrated with already developed Bucket Based Feature Selection Technique (BBFST). BBFST is used as an internal part to rank features in a particular level of the tree. Results: Experimental work was carried out with open source WEKA data mining software using a 10-fold cross-validation technique. The proposed FST was compared with other ranking based FSTs to check the performance of C4.5 classifier with Phishing Email data set. Conclusion: The proposed FST reduces 33 features out of 47 features which exist in phishing Email dataset and C4.5 algorithm produces remarkable accuracy of 99.06% with only 11 features and it has been found to be better than other existing FSTs. © 2022 Bentham Science Publishers.

关键词： Decision trees

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A Blind Batch Encryption and Public Ledger-Based Protocol for Sharing Sensitive Data

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China Communications 2024年第1期21卷 310-322页

作者： Zhiwei Wang Nianhua Yang Qingqing Chen Wei Shen Zhiying Zhang School of Computer Sciences Nanjing University of Posts and TelecommunicationsNanjing 210023China Dept.of Intelligent Science and Information Law East China University of Political Science and LawShanghai 201620China Key Laboratory of Computational Science and Application of Hainan Province Hainan Normal UniversityHaikou 571158China State Key Laboratory of Public Big Data Guizhou UniversityGuizhou 550025China Yunnan Key Laboratory of Blockchain Application Technology Yunnan Innovation Research Institute of Beijing University of Aeronautics and AstronauticsKunming 650233

For the goals of security and privacy preservation,we propose a blind batch encryption-and public ledger-based data sharing protocol that allows the integrity of sensitive data to be audited by a public ledger and allows privacy information to be *** owners can tightly manage their data with efficient revocation and only grant one-time adaptive access for the fulfillment of the *** prove that our protocol is semanticallly secure,blind,and secure against oblivious requesters and malicious file *** also provide security analysis in the context of four typical attacks.

关键词： blind batch encryption data sharing onetime adaptive access public ledger security and privacy

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Make U-Net Greater: An Easy-to-Embed Approach to Improve Segmentation Performance Using Hypergraph

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computer Systems Science & Engineering 2022年第7期42卷 319-333页

作者： Jing Peng Jingfu Yang Chaoyang Xia Xiaojie Li Yanfen Guo Ying Fu Xinlai Chen Zhe Cui Chengdu Institute of Computer Application University of Chinese Academy of SciencesChengdu610041China School of Computer Science Chengdu University of Information TechnologyChengdu610225China University of Chinese Academy of Sciences Beijing100049China Department of Experimental Rheumatology Nijmegen6525GANetherlands

semantics information while maintaining spatial detail ***-range context information plays a crucial role in this ***-ever,the traditional convolution kernel only provides the local and small size of the receptiveﬁ*** address the problem,we propose a plug-and-play module aggregating both local and global information(aka LGIA module)to capture the high-order relationship between nodes that are far *** incorporate both local and global correlations into hypergraph which is able to capture high-order rela-tionships between nodes via the concept of a hyperedge connecting a subset of *** local correlation considers neighborhood nodes that are spatially adja-cent and similar in the same CNN feature maps of magnetic resonance(MR)image;and the global correlation is searched from a batch of CNN feature maps of MR images in feature *** inﬂuence of these two correlations on seman-tic segmentation is *** validated our LGIA module on various CNN segmentation models with the cardiac MR images *** results demonstrate that our approach outperformed several baseline models.

关键词： Convolutional neural network semantic segmentation hypergraph neural network LGIA module

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