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The Immense Impact of Reverse Edges on Large Hierarchical networks

engineering 2024年第5期36卷 240-249页

作者： Haosen Cao Bin-Bin Hu Xiaoyu Mo Duxin Chen Jianxi Gao Ye Yuan Guanrong Chen Tamás Vicsek Xiaohong Guan Hai-Tao Zhang MOE Engineering Research Center of Autonomous Intelligent Unmanned Systems State Key Laboratory of Intelligent Manufacturing Equipment and TechnologySchool of Artificial Intelligence and AutomationHuazhong University of Science and TechnologyWuhan 430074China School of Mechanical and Aerospace Engineering Nanyang Technological UniversitySingapore 639798Singapore Jiangsu Key Laboratory of Networked Collective Intelligence School of MathematicsSoutheast UniversityNanjing 210096China Department of Computer Science Rensselaer Polytechnic InstituteTroyNY 12180USA Department of Electronic Engineering City University of Hong KongHong Kong 999077China Department of Biological Physics Eötvös UniversityBudapest 1117Hungary MOE Key Laboratory for Intelligent Networks and Network Security School of Automation Science and EngineeringXi’an Jiaotong UniversityXi’an 710049China

Hierarchical networks are frequently encountered in animal groups,gene networks,and artificial engineering systems such as multiple robots,unmanned vehicle systems,smart grids,wind farm networks,and so *** structure of a large directed hierarchical network is often strongly influenced by reverse edges from lower-to higher-level nodes,such as lagging birds’howl in a flock or the opinions of lowerlevel individuals feeding back to higher-level ones in a social *** study reveals that,for most large-scale real hierarchical networks,the majority of the reverse edges do not affect the synchronization process of the entire network;the synchronization process is influenced only by a small part of these reverse edges along specific *** surprisingly,a single effective reverse edge can slow down the synchronization of a huge hierarchical network by over 60%.The effect of such edges depends not on the network size but only on the average in-degree of the involved *** overwhelming majority of active reverse edges turn out to have some kind of“bunching”effect on the information flows of hierarchical networks,which slows down synchronization *** finding refines the current understanding of the role of reverse edges in many natural,social,and engineering hierarchical networks,which might be beneficial for precisely tuning the synchronization rhythms of these *** study also proposes an effective way to attack a hierarchical network by adding a malicious reverse edge to it and provides some guidance for protecting a network by screening out the specific small proportion of vulnerable nodes.

关键词： Synchronizability Large hierarchical networks Reverse edges Information flows Complex networks

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Multimodal Dialogue Emotion Recognition Based on Label Optimization and Coarse-Grained Assisted Fine-Grained

Multimodal Dialogue Emotion Recognition Based on Label Optim...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Zhibo Zhang Zhenyu Yang Yan Huang Baojie Xu Wenyue Hu Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Qilu University of Technology (Shandong Academy of Sciences) China Shandong Provincial Key Laboratory of Industrial Network and Information System Security Shandong Fundamental Research Center for Computer Science China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Multimodal dialogue emotion recognition integrates data from multiple modalities to accurately identify emotional states in conversations. However, differences in expression and information density across modalities complicate the fusion of features. Traditional methods may introduce redundant information from other utterances, reducing the accuracy of emotion recognition. Existing one-hot labels often fail to capture the full range of emotional expressions, leading to biased results. To address these issues, we propose a model that fuses different modalities within the same utterance to avoid redundancy. It employs a progressive classification process, refining emotion recognition from coarse to fine granularity. Additionally, we use emotion polarity probabilities as weights for fine-grained classification and introduce a multimodal information-rich label that considers both the data and their interactions. Experiments on IEMOCAP and MELD datasets demonstrate the model’s effectiveness, significantly improving dialog emotion recognition accuracy. Our code is available at https://***/r/LOCG-188E.

关键词： Emotion recognition Accuracy Fuses Refining Termination of employment Speech recognition Signal processing Speech processing Optimization Faces

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Quantum Run-length Encoding: Optimizing Data Compression on Quantum computers with Exponential Resource Efficiency

Quantum Run-length Encoding: Optimizing Data Compression on ...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Jiale Zhang Xilong Che Shiyong Jin Kaifan Pan Shun Peng Juncheng Hu College of Computer Science and Technology Jilin University Changchun China College of Intelligent Science and Engineering Harbin Engineering University Harbin China Computer Network Information Center University of Chinese Academy of Science Beijing China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Quantum computers, leveraging superposition and entanglement, offer significant qubit efficiency for data processing compared to classical systems. However, encoding classical data into quantum states, given the current limitations of quantum hardware, often results in higher runtime complexity than classical methods, thus limiting the perceived quantum advantage. Previous quantum data compression methods, primarily based on Amplitude Encoding and mixed-state systems, result in lossy data recovery and necessitate extensive preprocessing. In this work, we propose Quantum Run-Length Encoding (QRLE), a novel lossless quantum data compression method that integrates Basic Encoding with Run-Length Encoding principles. By encoding repeated data sequences with their run lengths, QRLE achieves efficient and accurate data recovery on quantum computers, while exponentially reducing both qubit costs and runtime complexity compared to existing quantum data storage models. We further explore QRLE’s application in image processing, where it significantly optimizes quantum resource utilization over recent quantum image representation techniques. Experiments conducted on both quantum simulators and IBM’s superconducting quantum computer validate the efficiency of QRLE and confirm its compatibility with current quantum hardware.

关键词： computers Training Quantum computing Image coding Runtime Computational modeling Qubit Data compression Hardware Complexity theory

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A Fault Tracing Method Based on Knowledge Graph and Weighted Path Ranking Algorithm

A Fault Tracing Method Based on Knowledge Graph and Weighted...

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Prognostics and System Health Management Conference (PHM-Qingdao)

作者： Zibo Wang Honghui Li Junwen Zhang Xiaorui Yang Nahao Shen School of Computer Science and Technology Beijing Jiaotong University Beijing China Engineering Research Center of Network Management Technology For High Speed Railway of MOE Beijing China

ISBN: (数字)9798350354010

ISBN: (纸本)9798350354027

The structure of railway systems is complex, and accurately and rapidly identifying the root cause of faults post-occurrence is an emerging challenge in the industry. However, railway fault tracing mainly relies on manual experience and traditional data analysis techniques, with most tracing methods suffering from low efficiency and insufficient accuracy. To address these issues, this paper proposes a railway safety equipment fault tracing method based on knowledge graphs and weighted path ranking algorithm. The approach integrates knowledge graphs with Bayesian networks, attention mechanisms, and graph computation, utilizing a weighted calculation to run the weighted path ranking algorithm. Experimental results demonstrate that the proposed method achieves excellent tracing performance in the field of railway equipment.

关键词： Fault diagnosis Accuracy Machine learning algorithms Heuristic algorithms Computational modeling Railway safety Knowledge graphs Rail transportation Data mining Sorting

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列车-轨道-地基土耦合系统三维随机振动的多GPU并行计算方法

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Journal of Central South University 2023年第5期30卷 1722-1736页

作者：朱志辉杨啸李昊徐海坤邹有 National Engineering Research Center of High-speed Railway Construction Technology Central South UniversityChangsha 410075China School of Civil Engineering Central South UniversityChangsha 410075China School of Computer Science and Engineering Central South UniversityChangsha 410083China Information and Network Center Central South UniversityChangsha 410083China

针对列车-轨道-地基土耦合系统随机计算效率低的问题,本文提出了基于多GPU的列车-轨道-地基土随机振动方程的高效并行计算方法。基于OpenMP-CUDA混编技术将虚拟激励法不同频点下的多个线性方程组求解任务分配给多个GPU并行执行;在每块GP... 详细信息

针对列车-轨道-地基土耦合系统随机计算效率低的问题,本文提出了基于多GPU的列车-轨道-地基土随机振动方程的高效并行计算方法。基于OpenMP-CUDA混编技术将虚拟激励法不同频点下的多个线性方程组求解任务分配给多个GPU并行执行;在每块GPU上,采用基于CUDA的预处理共轭梯度法(PCG)并行求解对称正定的等效静力平衡方程。针对耦合系统等效刚度矩阵的稀疏特性,采用行压缩(CSR)格式存储大型稀疏矩阵以节省内存空间。最终通过MATLAB-CUDA混合平台开发并行计算程序,解决了随机振动分析中多个线性方程组串行求解效率低的难题。数值算例表明,基于四GPU节点的多GPU并行算法和单GPU加速PCG算法的计算效率是串行多点同步算法(MPSA)计算效率的22.59倍和3.75倍。

关键词：随机振动并行计算多GPU 三维有限元法列车-轨道-地基土耦合模型

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Joint Resource Allocation and Blocklength Assignment in Star-Ris and Noma-Assisted Urllc Systems

SSRN

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SSRN 2024年

作者： Yang, Shuhui Chen, Jian Jie, Jia Guo, Liang Wang, Xingwei School of Computer Science and Engineering Northeastern University Liaoning Shenyang110819 China Engineering Research Center of Security Technology of Complex Network System Ministry of Education China

This paper investigates a novel non-orthogonal multiple access (NOMA) assisted ultra-reliable low-latency communications (URLLC) paradigm. By allowing multiple users to occupy the same time or frequency resource blocks with the NOMA technique, the transmission delays and network congestion in multi-user URLLC communication systems are thus mitigated. Furthermore, the simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) is employed to provide full-space coverage by proactively reconfiguring the electromagnetic propagation environment. The energy consumption optimization problem is formulated by refining blocklength assignment and jointly optimizing the power allocation and the STAR-RIS configuration. Due to highly coupled variables and the non-convexity of constraints, the optimization problem is proven to be NP-hard. To tackle the intractable problem, we propose an alternating optimization (AO)-based solution combining adaptive genetic algorithm (AGA), and successive convex approximation (SCA) methods. Numerical results demonstrate that: i) the proposed refined blocklength assignment scheme outperforms that with fixed blocklength assignment;ii) the proposed STAR-RIS-assisted NOMA system is superior to the conventional RIS-assisted NOMA system. © 2024, The Authors. All rights reserved.

关键词： Resource allocation

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Cognitive Defense Cyber Attack Prediction and Security Design in Machine Learning Model 6

Cognitive Defense Cyber Attack Prediction and Security Desig...

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

作者： Devadarshini, P.T. Chandrashekar, B. Pundir, Sumit Tiwari, Mohit Madala, Ravikiran Indhuma, E. Easwari Engineering College Department of Artificial Intelligence and Data Science TamilNadu Chennai India St. Martin's Engineering College Department of Computer Science and Engineering Kompalli Telangana Secunderabad India Graphic Era Deemed to Be University Department of Computer Science and Engineering Uttrakhand Dehradun India Bharati Vidyapeeth's College of Engineering Department of Computer Science and Engineering Delhi India Network and Data Center Engineer Ph.D Information Technology University of Cumberlands DurhamNC United States St.Martln's Engineering College Department of Computer Science and Engineering Telangana Secunderabad India

ISBN: (纸本)9798350304480

Companies of all sizes in today's linked digital world are susceptible to cyber dangers. In this study, we provide a novel approach to cyber defence that integrates machine learning with cognitive defensive techniques to identify and prevent cyber attacks in their tracks. We built three ML models that use cognitive defensive strategies and other cutting-edge security measures like encryption and intrusion detection. A 95% accuracy rate was reached by our models, which is far greater than where things stand right now models. Our work has the potential to have a huge effect since the models we've developed may one day help businesses take a more preventative stance against cyber threats. Our findings may potentially be used to fields outside than cyber security. Overall, our study shows that machine learning and cognitive defense are useful for foreseeing and stopping cyber threats, and points the way toward fruitful future study in this area. © 2023 IEEE.

关键词： Accuracy Advanced security features Cognitive defense Cyber security Cyber-attacks Encryption Intrusion detection Machine learning Prediction Prevention Proactive approach Threat landscape

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Deep Learning for Object Detection:A Survey

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computer Systems Science & engineering 2021年第8期38卷 165-182页

作者： Jun Wang Tingjuan Zhang Yong Cheng Najla Al-Nabhan Science and Technology Industry Department Nanjing University of Information Science&Technology210044China School of Computer&Software Jiangsu Engineering Center of Network MonitoringNanjing University of Information Science&Technology210044China Department Computer Science King Saud UniversityRiyadhKSA

Object detection is one of the most important and challenging branches of computer vision,which has been widely applied in people s life,such as monitoring security,autonomous driving and so on,with the purpose of locating instances of semantic objects of a certain *** the rapid development of deep learning algorithms for detection tasks,the performance of object detectors has been greatly *** order to understand the main development status of target detection,a comprehensive literature review of target detection and an overall discussion of the works closely related to it are presented in this *** paper various object detection methods,including one-stage and two-stage detectors,are systematically summarized,and the datasets and evaluation criteria used in object detection are *** addition,the development of object detection technology is ***,based on the understanding of the current development of target detection,we discuss the main research directions in the future.

关键词： Object detection convolutional neural network computer vision

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Combining Fourier-Based Channel Attention with TCN and Informer for Railway Accident Prediction

Combining Fourier-Based Channel Attention with TCN and Infor...

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Prognostics and System Health Management Conference (PHM-Qingdao)

作者： Nahao Shen Honghui Li Yanhui Bai Xiaorui Yang Ruiyi He School of Computer Science and Technology Beijing Jiaotong University Beijing China School of Computer Science and Technology Beijing Jiaotong University Engineering Research Center of Network Management Technology for High Speed Railway of MOE Beijing China

ISBN: (数字)9798350354010

ISBN: (纸本)9798350354027

The rapid expansion of railway networks and technological advancements has introduced significant challenges in ensuring railway safety. To address the critical need for effective prediction and prevention mechanisms, this paper proposes the FCAM-TCN-DSInformer model, a novel approach to railway accident prediction. The model integrates the Fourier-based Channel Attention Mechanism (FCAM), Temporal Convolutional network (TCN), and De-stationary Informer (DSInformer) to capture both temporal and spectral features from time series data effectively. Extensive experiments and ablation studies were conducted to validate the model's performance. The results demonstrate that the FCAM-TCN-DSInformer model significantly outperforms both traditional and advanced models, including ARIMA, LSTM, GRU, Transformer, Autoformer, and FEDformer by achieving the highest $\mathrm{R}^{{2}}$ score and substantial reductions in error metrics such as RMSE, MAE, and MAPE. These improvements confirm the model's accuracy and robustness, making it a powerful tool for railway safety management. Moreover, the model's adaptability to different univariate time series datasets, such as the Daily Minimum Temperatures Dataset and the US Births Dataset, highlights its strong generalization capabilities. Future work will focus on optimizing the model for real-time predictions and expanding its application to multi-step prediction tasks, thereby fully realizing its potential across diverse forecasting contexts.

关键词： Adaptation models Attention mechanisms Computational modeling Railway accidents Time series analysis Railway safety Predictive models Transformers Data models Forecasting

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Maximizing Energy Efficiency of HSRC: A Cache-Aided Time-Domain Power Allocation 15

Maximizing Energy Efficiency of HSRC: A Cache-Aided Time-Dom...

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15th IEEE International Conference on Wireless Communications and Signal Processing, WCSP 2023

作者： Li, Yeshen Xiong, Ke Li, Han Ai, Bo Fan, Pingyi Letaief, Khaled Ben Beijing Jiaotong University Eng. Res. Center of Network Management Technology for High Speed Railway of Ministry of Education School of Computer and Information Technology Beijing100044 China Beijing Jiaotong University Collaborative Innovation Center of Railway Traffic Safety Beijing100044 China Beijing Jiaotong University State Key Laboratory of Electronic and Information Engineering School Beijing100044 China Tsinghua University Department of Electronic Engineering Beijing100084 China Department of Electrical and Computer Engineering Hong Kong

ISBN: (纸本)9798350324662

This paper presents a cache-aided time-domain power allocation (CaTDPA) for 5G high-speed railway communications (HSRC), where two kinds of users, online interactive application users (OIUs) and online non-interactive application users (ONIUs), are jointly taken into account. For OIUs, their minimum ground-train data rate demand is guaranteed. For ONIUs, their minimum cache-releasing data rate demand is guaranteed. To achieve the green HSRC design, an optimization problem is formulated to maximize the system energy efficiency (EE) by jointly optimizing the power allocation (PA) and cache releasing start time (CRST) subject to the constraints of total available energy, maximum transmit power, ONIUs' ground-train data rate threshold, ONIUs' cache-releasing data rate threshold, and causality of CRST. To address such a non-convex and fractional problem, a two-step solution is designed. First, with a given CRST, a Dinkelbach-based method is applied to obtain the correspondingly optimal PA. Second, the one-dimensional traversal method is used to find the optimal CRST. Experimental results show that our proposed CaTDPA achieves the maximum EE among multiple compared methods while meeting the requirements of OIUs and ONIUs. Moreover, EE will decrease as the OIUs' ground-train data rate threshold and the train's moving speed increase, while it is observed that the ONIUs' cache-releasing data rate threshold has a relatively small effect on the EE of HSRC. © 2023 IEEE.

关键词： cache Energy efficiency high-speed railway power allocation

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