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Hyper-Tuned Convolutional Neural Networks for Authorship Verification in Digital Forensic Investigations

Computers, Materials & Continua 2023年第8期76卷 1947-1976页

作者： Asif Rahim Yanru Zhong Tariq Ahmad Sadique Ahmad Mohammed A.ElAffendi School of Computer and Information Security Guilin University of Electronic TechnologyGuilin541004China Guangxi Key Laboratory of Intelligent Processing of Computer Images and Graphics Guilin University of Electronic TechnologyGuilin541004China School of Information and Communication Guilin University of Electronic TechnologyGuilin541004China EIAS:Data Science and Blockchain Laboratory College of Computer and Information SciencesPrince Sultan UniversityRiyadh11586Saudi Arabia

Authorship verification is a crucial task in digital forensic investigations,where it is often necessary to determine whether a specific individual wrote a particular piece of *** Neural Networks(CNNs)have shown promise in solving this problem,but their performance highly depends on the choice of *** this paper,we explore the effectiveness of hyperparameter tuning in improving the performance of CNNs for authorship *** conduct experiments using a Hyper Tuned CNN model with three popular optimization algorithms:Adaptive Moment Estimation(ADAM),StochasticGradientDescent(SGD),andRoot Mean Squared Propagation(RMSPROP).The model is trained and tested on a dataset of text samples collected from various authors,and the performance is evaluated using accuracy,precision,recall,and F1 *** compare the performance of the three optimization algorithms and demonstrate the effectiveness of hyperparameter tuning in improving the accuracy of the CNN *** results show that the Hyper Tuned CNN model with ADAM Optimizer achieves the highest accuracy of up to 90%.Furthermore,we demonstrate that hyperparameter tuning can help achieve significant performance improvements,even using a relatively simple model architecture like *** findings suggest that the choice of the optimization algorithm is a crucial factor in the performance of CNNs for authorship verification and that hyperparameter tuning can be an effective way to optimize this ***,this paper demonstrates the effectiveness of hyperparameter tuning in improving the performance of CNNs for authorship verification in digital forensic *** findings have important implications for developing accurate and reliable authorship verification systems,which are crucial for various applications in digital forensics,such as identifying the author of anonymous threatening messages or detecting cases of plagiarism.

关键词： Convolutional Neural Network(CNN) hyper-tuning authorship verification digital forensics

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Robust Ascent Trajectory Optimization for Hypersonic Vehicles Based on IGS-UMPSP

Robust Ascent Trajectory Optimization for Hypersonic Vehicl...

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Chinese intelligent Automation Conference, CIAC 2023

作者： Qi, Yuting Wang, Bo Liu, Lei Fan, Huijin Wang, Yongji National Key Laboratory of Multispectral Information Intelligent Processing Technology School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China

ISBN: (纸本)9789819961863

This paper proposes a new approach for solving trajectory optimization problems with uncertain system parameters, which is the generalized quasi-spectral version of unscented model predictive static programming. The proposed method combines two existing techniques, the generalized quasi-spectral model predictive static programming and the unscented optimal control, to generate control solutions that can meet multiple constraints, while minimizing the covariance of the terminal output. The technique has shown good robustness in simulations and has been demonstrated to be effective in solving ascent trajectory optimization problems of hypersonic vehicles, providing a reference trajectory for guidance. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2023.

关键词： Hypersonic vehicles

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Impact Investigation of Adversarial Samples on CNN-Based SAR Image Target Detectors 6

Impact Investigation of Adversarial Samples on CNN-Based SAR...

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6th IEEE International Conference on Pattern Recognition and Artificial Intelligence, PRAI 2023

作者： Zhou, Peng Xiao, Shunping Li, Mingdian Deng, Junwu Xing, Xiangwei Chen, Siwei National University of Defense Technology College of Electronic Science and Technology Changsha China Satellite Information Intelligent Processing and Application Research Laboratory Beijing China

ISBN: (纸本)9798350325485

In recent years, deep learning algorithms based on convolutional neural networks (CNN) have made breakthroughs in the field of target detection in Synthetic Aperture Radar (SAR) images. However, CNN may be vulnerable to adversarial examples, raising security concerns for practical deployment. To gain a deeper understanding of adversarial examples and provide a theoretical basis for building robust detection networks, this study comprehensively investigates the impacts of adversarial samples generated by deep recognition networks (DRN) in SAR images on the performance of target detection networks with different principles, using YOLOv3 and Faster-RCNN as examples. Experimental results on the MiniSAR vehicle target dataset show that adversarial samples pose a more serious threat to the single-stage detection network YOLOv3. Simply adding small perturbation that are invisible for human eyes to the SAR image can mislead the detector into missing the majority of targets. In contrast, two-stage detection networks like Faster-RCNN exhibit stronger robustness with their detection performance being less influenced by the adversarial examples generated by different DRN. © 2023 IEEE.

关键词： Synthetic aperture radar

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Clipping discrete multi-tone for peak-power-constraint IM/DD optical systems

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Science China(information Sciences) 2023年第5期66卷 227-237页

作者： Ji ZHOU Liangchuan LI Jiale HE Xiaofeng LU Yu BO Guanyu WANG Yuanda HUANG Gengchen LIU Yanzhao LU Shecheng GAO Yuanhua FENG Shancheng ZHAO Weiping LIU Changyuan YU Zhaohui LI Department of Electronic Engineering College of Information Science and Technology Jinan University Optical Research Department Huawei Technologies Co. Ltd. Department of Electronic and Information Engineering The Hong Kong Polytechnic University Guangdong Provincial Key Laboratory of Optoelectronic Information Processing Chips and Systems Sun Yat-sen University Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai)

High peak-to-average power ratio(PAPR) is a major drawback of discrete multi-tone(DMT) in peak-power-constraint(PPC) intensity-modulation/direct-detection(IM/DD) optical systems. Symmetric clipping operation is a frequently-used and practical method for mitigating the PAPR of the DMT ***, clipping noise is inevitably induced by the symmetric clipping operation, thereby deteriorating the system's performance. In this paper, we study the statistical characteristics of the clipping noise in *** on the statistical characteristics, clipping-noise-cancellation(CNC) algorithms are proposed to mitigate the clipping noise in the clipping DMT systems. Theoretically, the clipping noise can be completely removed using the low-density-parity-check-assisted CNC algorithm(LDPC-assisted CNC algorithm). To verify the superiority of the clipping DMT with the CNC algorithm, we conduct an experiment with a50 Gb/s DMT system using 10G-class commercial devices for the scenario of PPC passive optical *** receiver sensitivity of the clipping DMT system with the CNC algorithm can reach-24 d Bm at a 20%soft-decision forward-error-correction limit, which is 2.5 d B higher than that of the DMT system without clipping operation. The clipping operation and the CNC algorithm are added after the original digital signal processing, thereby demonstrating the potential to efectively overcome the drawback in DMT signals and other high-PAPR signals.

关键词： discrete multi-tone peak-to-average power ratio peak-power constraint clipping-noise cancellation intensity-modulation/direct-detection optical systems

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Pretrained Models and Evaluation Data for the Khmer Language

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Tsinghua Science and technology 2022年第4期27卷 709-718页

作者： Shengyi Jiang Sihui Fu Nankai Lin Yingwen Fu School of Information Science and Technology Guangdong University of Foreign StudiesGuangzhou 510000China Guangzhou Key Laboratory of Multilingual Intelligent Processing Guangdong University of Foreign StudiesGuangzhou 510000China

Trained on a large corpus,pretrained models(PTMs)can capture different levels of concepts in context and hence generate universal language representations,which greatly benefit downstream natural language processing(NLP)*** recent years,PTMs have been widely used in most NLP applications,especially for high-resource languages,such as English and ***,scarce resources have discouraged the progress of PTMs for low-resource ***-based PTMs for the Khmer language are presented in this work for the first *** evaluate our models on two downstream tasks:Part-of-speech tagging and news *** dataset for the latter task is *** demonstrate the effectiveness of the Khmer *** addition,we find that the current Khmer word segmentation technology does not aid performance *** aim to release our models and datasets to the community in hopes of facilitating the future development of Khmer NLP applications.

关键词： pretrained models Khmer language word segmentation part-of-speech(POS)tagging news categorization

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Robust Attitude Control for Flying-Wing UAV Using Modified Incremental Nonlinear Dynamic Inversion

Robust Attitude Control for Flying-Wing UAV Using Modified I...

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2023 China Automation Congress, CAC 2023

作者： Li, Wangmian Xue, Chenlong Wang, Bo Liu, Lei Fan, Huijin Yang, Jiahao School of Artificial Intelligence and Automation Huazhong University of Science and Technology National Key Laboratory of Multispectral Information Intelligent Processing Technology Wuhan430074 China

ISBN: (纸本)9798350303759

This paper proposes a modified incremental nonlinear dynamic inversion (MINDI) control scheme for attitude tracking control of flying-wing unmanned aerial vehicles (UAV). Based on the incremental dynamic inversion formulation, the angular rate controller is designed to alleviate the nonlinearity and coupling characteristics of the flying-wing configuration. Then, in order to address the impact of factors such as angular acceleration measurement errors and actuator faults on the control performance, a super-twisting-like fixed-time convergence algorithm is introduced to replace the linear control law in the nominal INDI method, ensuring rapid convergence of tracking errors. The simulation results demonstrate that the proposed method effectively mitigates the impact of such errors and faults. Compared to the nominal INDI, the modified method exhibits stronger fault tolerance and robustness in the attitude tracking control of the flying-wing UAV. © 2023 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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Design technology of Multi-AGV Warehouse intelligent Scheduling Optimization Control System 15th

Design Technology of Multi-AGV Warehouse Intelligent Schedul...

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15th International Conference on Genetic and Evolutionary Computing, ICGEC 2023

作者： Pan, YanPeng Zhang, XueTing Zhang, FuQuan Tan, LiLi Yang, LiJie Ma, Jun China Academy of Launch Vehicle Technology Beijing100076 China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Minjiang University Fuzhou350121 China

ISBN: (纸本)9789819700677

Different from the traditional warehousing system, in the multi-AGV warehouse scheduling control system, the constraints of poor connectivity of the conveying track have led to the emergence of urgent problems such as vehicle conflicts in the task, low efficiency of multitasking concurrency, difficulty of delineating the conditions for entering the transporting area and so on. This paper proposes a design of multi-AGV warehouse intelligent scheduling optimized control system, which takes multitask scheduling research as an entry point, designs the warehouse layout and vehicle operation rules assumptions and modeling, studies the scheduling solution of Monorail Vehicles in multiple tasks, designs a scheduling optimization method based on the algorithm of Time Window, solves conflicts between key process and operation task process and optimize the scheduling, so as to effectively enhance the efficient scheduling capability of multi-AGV warehouse intelligent scheduling optimized control system. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Warehouses

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Identifying Influential Spreaders in Complex Networks Using Neighborhood Network Structure 35

Identifying Influential Spreaders in Complex Networks Using ...

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35th International Conference on Software Engineering and Knowledge Engineering, SEKE 2023

作者： Zhang, Ziyi Yan, Rong Yuan, Wei College of Computer Science Inner Mongolia University Inner Mongolia Key Laboratory of Mongolian Information Processing Technology National & Local Joint Engineering Research Center of Intelligent Information Processing Technology for Mongolian Hohhot010021 China

Identifying influential spreaders is a hot topic in complex network research. While centrality-based algorithms are easy to implement, they often have lower accuracy. Topology-based algorithms are effective for identifying network center influential spreaders but may not perform well in identifying peripheral influential spreaders. To address these problems, we propose a Neighborhood Structure Centrality (NSC) algorithm, which utilizes structural embedding and clustering to collect various network structural information and calculates node influence based on both node and neighborhood structural information. We compare the NSC algorithm with twelve baseline algorithms on six public datasets and four synthetic network datasets and demonstrate its higher accuracy. © 2023 Knowledge systems Institute Graduate School. All rights reserved.

关键词： Complex networks

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Pedestrian wind flow prediction using spatial-frequency generative adversarial network

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Building Simulation 2024年第2期17卷 319-334页

作者： Pengyue Wang Maozu Guo Yingeng Cao Shimeng Hao Xiaoping Zhou Lingling Zhao School of Architecture and Urban Planning Beijing University of Civil Engineering and ArchitectureBeijing100044China Beijing Key Laboratory of Intelligent Processing for Building Big Data Beijing100044China School of Electrical and Information Engineering Beijing University of Civil Engineering and ArchitectureBeijing100044China School of Computer Science and Technology Harbin Institute of TechnologyHarbin150001China

Pedestrian wind flow is a critical factor in designing livable residential environments under growing complex urban *** pedestrian wind flow during the early design stages is essential but currently suffers from inefficiencies in numerical *** learning,particularly generative adversarial networks(GAN),has been increasingly adopted as an alternative method to provide efficient prediction of pedestrian wind ***,existing GAN-based wind flow prediction schemes have limitations due to the lack of considering the spatial and frequency characteristics of wind flow *** study proposes a novel approach termed SFGAN,which embeds spatial and frequency characteristics to enhance pedestrian wind flow *** the spatial domain,Gaussian blur is employed to decompose wind flow into components containing wind speed and distinguished flow edges,which are used as the embedded spatial *** information of wind flow is obtained through discrete wavelet transformation and used as the embedded frequency *** spatial and frequency characteristics of wind flow are jointly utilized to enforce consistency between the predicted wind flow and ground truth during the training phase,thereby leading to enhanced *** results demonstrate that SFGAN clearly improves wind flow prediction,reducing Wind_MAE,Wind_RMSE and the Fréchet Inception Distance(FID)score by 5.35%,6.52%and 12.30%,compared to the previous best method,*** also analyze the effectiveness of incorporating the spatial and frequency characteristics of wind flow in predicting pedestrian wind *** reduces errors in predicting wind flow at large error intervals and performs well in wake regions and regions surrounding *** enhanced predictions provide a better understanding of performance variability,bringing insights at the early design stage to improve pedestrian wind *** proposed spatial-frequen

关键词： pedestrian wind flow prediction generative adversarial network Gaussian kernel wavelet transform objective function

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SSD object detection algorithm based on knowledge map

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International Journal of Wireless and Mobile Computing 2024年第2期27卷 152-160页

作者： Huang, Li Wang, Xiaofeng Lu, Jianhua Hu, Wei Zhang, Changrong College of Computer Science and Technology Wuhan University of Science and Technology Hubei Wuhan China China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan University of Science and Technology Hubei Wuhan China Key Laboratory of Metallurgical Equipment and Control Technology of the Ministry of Education Wuhan University of Science and Technology Hubei Wuhan China Precision Manufacturing Research Institute Research Centre for Biomimetic Robot and Intelligent Measurement and Control Wuhan University of Science and Technology Hubei Wuhan China

With the pervasive integration of artificial intelligence into all aspects of human life, talent emerges as a primary resource. Upon analysing the current state of talent training in higher education institutions, issues such as dispersed knowledge points, overlapping content, a singular practice approach and ineffective evaluation have been identified. In response to these challenges, this paper proposes a multidisciplinary and comprehensive practical teaching methodology grounded in the knowledge graph framework. It delves into diverse paths for practical teaching and assessment, including aspects like teaching objectives, problem decomposition, resource integration, implementation methods and performance evaluation. The practical application of the SSD algorithm in researching service robot indoor object detection serves as an illustrative example. Employing the holistic practical teaching approach facilitated by the knowledge graph, this model guides students in acquiring object detection expertise, thereby enhancing their comprehensive development. Copyright © 2024 Inderscience Enterprises Ltd.

关键词： Object detection

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