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IEEE International Symposium on Biomedical Imaging

作者： Pengcheng Lei Jiarui Xing Faming Fang Frederick H. Epstein Miaomiao Zhang Department of Electrical and Computer Engineering University of Virginia USA School of Computer Science and Technology East China Normal University Shanghai China Department of Biomedical Engineering University of Virginia Health System USA

ISBN: (数字)9798331520526

ISBN: (纸本)9798331520533

Myocardial strain imaging provides a valuable tool for detecting subclinical left ventricular (LV) dysfunction and adding prognostic value in assessing various types of heart disease. Recent studies have utilized highly accurate strain-dedicated techniques, such as displacement encoding with stimulated echoes (DENSE), to train a deep learning (DL) framework to predict the myocardial displacements/deformations from routine cine balanced steady state free precession (bSSFP) images. However, these methods have shown limited performance in capturing the large rotational motion of the myocardium associated with twist and torsion over time, which are important aspects of myocardial mechanics. To address this gap, this paper introduces a novel DENSE-guided DL network that explicitly accounts for large rotational motion to further improve strain analysis of standard cine bSSFP images. Specifically, our proposed network includes two key components: (i) a time-series rotation estimation network employing a 3D convolutional encoder-decoder architecture to model the large rotational dynamics of the LV myocardium over time, and (ii) a radial motion prediction network based on deformable image registration. The output of these two sub-networks was integrated and refined through a fusion network to predict the final myocardial displacements, supervised by DENSE ground truth. Experimental results show that our method improves the accuracy of myocardial strain with effectively captured large rotations.

关键词： Deep learning Solid modeling Accuracy Three-dimensional displays Magnetic resonance imaging Myocardium Predictive models Steady-state Standards Strain

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On Optimal Synchronization of Diffusively Coupled Heterogeneous Van der Pol Oscillators

arXiv

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arXiv 2023年

作者： Trummel, Tabea Liu, Zonglin Stursberg, Olaf Control and System Theory Dept. of Electrical Engineering and Computer Science University of Kassel Germany

This paper proposes a novel method to achieve and preserve synchronization for a set of connected heterogeneous Van der Pol oscillators. Unlike the state-of-the-art synchronization methods, in which a large coupling gain is applied to couple any pair of connected oscillators, the proposed method first casts the whole synchronization process into two phases. The first one considers the period from the beginning to the first instant of synchronization, while the second phase covers the following time in which synchronization must be preserved. It is shown that a large coupling gain is adopted for the first phase, while the averaged coupling gain to preserve the synchronization in the second phase can be reduced significantly by using an offline optimized coupling law. Efficiency and performance of this method are confirmed by a set of numerical tests with different graphs and system dynamics. © 2023, CC BY-NC-ND.

关键词： Synchronization

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The Impact of Stuttering Event Representation on Detection Performance

The Impact of Stuttering Event Representation on Detection P...

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Cyber Resilience (ICCR), International Conference on

作者： Abedal-Kareem Al-Banna Hui Fang Ahmad F Shubita Mohammad Arafah Amani Eqteeshat Saif B Abuaisheh Department of AI and Data Science University of Petra Department of Computer Science Loughborough University Department of Software Engineering University of Petra Department of Computer Science University of Petra Department of Information System Malaya University

ISBN: (数字)9798350394962

ISBN: (纸本)9798350394979

Stuttering or stammering is a common neurogenic, psychogenic fluency disorder wherein people have uncontrolled blocks on the natural flow of speech. Automatic Stuttering Event Detection (SED) is a widely recognised challenge due to the heterogenic and overlapped nature of stuttering speech. Therefore, this comparative study investigates the impact of stuttering event representation on detection performance. The paper evaluates the performance of SED and shows the impact of employing ASR pre-trained feature extractions on each stuttering event. Moreover, the paper provides different groups of experiments that evaluate the impact of three acoustic features, the Zero Crossing Rate (ZCR), the Spectral Flux Onset Strength Envelope (SFO), and Fundamental Frequency (FF) features, on SED performance. The paper demonstrates that the fusion of spectral and ASR features improves SED performance across all stuttering events by 8%. The F1 score of the Block class increased by approximately 10%, from 15% to 25%, while the word repetition and interjection increased by 8%. In addition, our experiments proved that the fusion of temporal features increased the average F1 score of SED by 2% across all stuttering events. The F1 score of the prolongation and sound repetition classes increased by 6% and 3%, respectively.

关键词： Event detection Speech recognition Feature extraction Acoustics Robustness Time-domain analysis Mel frequency cepstral coefficient

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Balance Multi-Head Attention based on Software and Hardware Co-design 9

Balance Multi-Head Attention based on Software and Hardware ...

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9th IEEE International Conference on Cyber Security and Cloud Computing and 8th IEEE International Conference on Edge Computing and Scalable Cloud, CSCloud-EdgeCom 2022

作者： Xu, Dian Hu, Wei Liu, Fang Fan, Zimeng Shi, Qingsong Wuhan University of Science and Technology Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System College of Computer Science Wuhan China Wuhan University Department of Information Engineering Wuhan Institute of City School of Computer Science Wuhan China Zhejiang University College of Computer Science Hangzhou China

ISBN: (数字)9781665480666

ISBN: (纸本)9781665480666

Recently, the Transformer-based models have achieved leading results in many research areas such as natural language processing and computer vision. However, since Transformer-based models have a huge computational complexities, optimization for Transformer is the focus of current research. the core of Transformer models is the attention module, and in this paper we propose a software and hardware co-designed attention module, which reduces the computational units of the multi-head attention module by balancing the The inference time of the attention module is reduced by balancing the computational units of multiple attention modules. At the same time, we design the corresponding structure on FPGA, which accelerate 14.6x inference time in comparison with the original attention module implementation on GPU. We applied this attention module to multiple visual Transformer models and tested the accuracy difference between the balanced multi-head attention module-based model and the original model on CIFAR-10, and obtained a result of 80.46%, which lower from the original version by 0.18%. © 2022 IEEE.

关键词： Field programmable gate arrays (FPGA)

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FDDSGCN: Fractional Decoupling Dynamic Spatiotemporal Graph Convolutional Network for Traffic Forecasting

FDDSGCN: Fractional Decoupling Dynamic Spatiotemporal Graph ...

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

作者： Jinpeng Xu Chunna Zhao Jing Yang Yaqun Huang Yaoyuan Yang Por Lip Yee School of Information Science & Engineering Yunnan University Kunming China Department of Computer System and Technology Universiti Malaya Kuala Lumpur Malaysia

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Urban traffic flow management faces increasing challenges due to accelerating urbanization. Traffic data collected from roadside sensors contain complex temporal and spatial dependencies that interact simultaneously. Although Graph Neural Networks and Recurrent Neural Networks have been successful in capturing these dependencies, two critical issues remain: 1) Treating all traffic signals equally fails to capture the nuanced spatiotemporal dependencies hidden in time series data; 2) Dynamic traffic conditions hinder the accurate capture of local spatial dependencies, thereby limiting prediction accuracy and reliability. To address these challenges, we propose an innovative model, FDDSGCN. To resolve the first issue, we introduce Fractional Residual Decomposition, which effectively separates traffic data into spatial and temporal signals. For the second issue, we employ Dynamic Spatial-Temporal Graph Convolution with fractional-order weight adjustments to dynamically capture local dependencies. Additionally, the Long Short-Term Dependency module analyzes both long-term and short-term dependencies. Extensive experiments on three public datasets demonstrate the superior performance and practical value of our model.

关键词： Accuracy Convolution Urban areas Time series analysis Spatial databases Spatiotemporal phenomena Sensors Speech processing Spatial resolution Signal resolution

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Graph Convolutional Neural Network Based Malware Detection in IoT-Cloud Environment

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Intelligent Automation & Soft Computing 2023年第6期36卷 2897-2914页

作者： Faisal SAlsubaei Haya Mesfer Alshahrani Khaled Tarmissi Abdelwahed Motwakel Department of Cybersecurity College of Computer Science and EngineeringUniversity of JeddahJeddah21959Saudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Sciences College of Computing and Information SystemUmm Al-Qura UniversityMakkah24211Saudi Arabia Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharj16242Saudi Arabia

Cybersecurity has become the most significant research area in the domain of the Internet of Things(IoT)owing to the ever-increasing number of *** rapid penetration of Android platforms in mobile devices has made the detection of malware attacks a challenging ***,Android malware is increasing on a daily ***,precise malware detection analytical techniques need a large number of hardware resources that are signifi-cantly resource-limited for mobile *** this research article,an optimal Graph Convolutional Neural Network-based Malware Detection and classification(OGCNN-MDC)model is introduced for an IoT-cloud *** pro-posed OGCNN-MDC model aims to recognize and categorize malware occur-rences in IoT-enabled cloud *** presented OGCNN-MDC model has three stages in total,such as data pre-processing,malware detection and para-meter *** detect and classify the malware,the GCNN model is exploited in this *** order to enhance the overall efficiency of the GCNN model,the Group Mean-based Optimizer(GMBO)algorithm is utilized to appropriately adjust the GCNN parameters,and this phenomenon shows the novelty of the cur-rent study.A widespread experimental analysis was conducted to establish the superiority of the proposed OGCNN-MDC model.A comprehensive comparison study was conducted,and the outcomes highlighted the supreme performance of the proposed OGCNN-MDC model over other recent approaches.

关键词： Cybersecurity IoT cloud malware detection graph convolution network

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Experimental Evaluation of Rate Adaptation using Deep-Q-Network in IEEE 802.11 WLAN

Experimental Evaluation of Rate Adaptation using Deep-Q-Netw...

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Consumer Communications and Networking Conference, CCNC IEEE

作者： Muhammad Harry Bintang Pratama Tomoki Nakashima Yuhei Nagao Masayuki Kurosaki Hiroshi Ochi Grad. School of Computer Science and System Engineering Kyushu Institute of Technology Iizuka Japan

Rate adaptation algorithm has an important role in the Wi-Fi network. It ensures that the nodes transmit at a suitable transmission rate with minimum packet errors on the receiving side. However, there are cases when the existing algorithms fail to adapt to the changes in the communication environment. In this paper, we propose a rate adaptation algorithm using a Deep Q-network (DQN), in which the DQN agent controls the transmission rate of a node in response to the communication environment. We also evaluate the proposed algorithm using the field-programmable gate array (FPGA) and software-defined radio (SDR). The experimental result shows that the proposed algorithm can adaptively select the suitable MCS and maintain the throughput.

关键词： Adaptation models Simulation Throughput Real-time systems Field programmable gate arrays Wireless fidelity IEEE 802.11 Standard

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UICopilot: Automating UI Synthesis via Hierarchical Code Generation from Webpage Designs 25

UICopilot: Automating UI Synthesis via Hierarchical Code Gen...

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34th ACM Web Conference, WWW 2025

作者： Gui, Yi Wan, Yao Li, Zhen Zhang, Zhongyi Chen, Dongping Zhang, Hongyu Su, Yi Chen, Bohua Zhou, Xing Jiang, Wenbin Zhang, Xiangliang Huazhong University of Science and Technology Wuhan China Chongqing University Chongqing China Hubei University of Automotive Technology Shiyan China Rabbitpre AI Shenzhen China University of Notre Dame Notre Dame United States National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster Grid Computing Lab School of Computer Science and Technology China

ISBN: (纸本)9798400712746

Automating the synthesis of User Interfaces (UIs) plays a crucial role in enhancing productivity and accelerating the development lifecycle, reducing both development time and manual effort. Recently, the rapid development of Multimodal Large Language Models (MLLMs) has made it possible to generate front-end Hypertext Markup Language (HTML) code directly from webpage designs. However, real-world webpages encompass not only a diverse array of HTML tags but also complex stylesheets, resulting in significantly lengthy code. The lengthy code poses challenges for the performance and efficiency of MLLMs, especially in capturing the structural information of UI designs. To address these challenges, this paper proposes UICopilot, a novel approach to automating UI synthesis via hierarchical code generation from webpage designs. The core idea of UICopilot is to decompose the generation process into two stages: first, generating the coarse-grained HTML hierarchical structure, followed by the generation of fine-grained code. To validate the effectiveness of UICopilot, we conduct experiments on a real-world dataset, i.e., WebCode2M. Experimental results demonstrate that UICopilot significantly outperforms existing baselines in both automatic evaluation metrics and human evaluations. Specifically, statistical analysis reveals that the majority of human annotators prefer the webpages generated by UICopilot over those produced by GPT-4V. © 2025 Copyright held by the owner/author(s).

关键词： Web Design

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Beam shaping based on a silicon optical phased array chip

Beam shaping based on a silicon optical phased array chip

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IEEE International Conference on Group IV Photonics

作者： Wenbo Ren Haipeng Liu Wei Jiang Xincheng Xia Zejun Yan Jijun Feng Shanghai Key Laboratory of Modern Optical System Engineering Research Center of Optical Instrument and System (Ministry of Education) School of Optical-Electrical and Computer Engineering University of Shanghai for Science and Technology Shanghai China

ISBN: (数字)9798350394047

ISBN: (纸本)9798350394054

In this study, we present a method for free-space beam shaping and steering based on a silicon optical phased array, which addresses the theoretical limitation of traditional bulk optics. We theoretically analyze the beam propagation properties with changes in the applied phase. Different beam profiles can be shaped by varying the phase combination, while a high-order quasi-Bessel beam can be generated with a cubic change to the phase modulation. The simulated results are validated further experimentally, and they match one another well. The presented method is expected to have broad application prospects for optical communications, free-space optical interconnects, and light detection and ranging.

关键词： Phased arrays Optical interconnections Free-space optical communication Phase modulation Optical propagation Silicon photonics Optics

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Review of National Cybersecurity Policies: A Case Study on Asian Countries

Review of National Cybersecurity Policies: A Case Study on A...

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Humanitarian Technology Conference (KHI-HTC), IEEE Karachi Section

作者： Hera Noor Dodo Khan Seelro Syed Abbas Ali Department of Computer Information System Engineering NED University of Engineering & Technology Karachi Pakistan Thar Institute of Engineering Science & Technology NED University of Engineering & Technology Karachi Pakistan

Accelerating the economic advantages of ICT is one of the finest methods for any nation to boost its economy. Due to the unique topography, governing laws, and cultural traditions of each nation, these colossal challenges must be confronted in each country. This comparative analysis aims to examine these aspects in further detail. To achieve the objectives of this study, research was conducted by collecting and analysing data from publicly accessible sources. This research compares Pakistan and other Asian countries’ cybersecurity legislation, regulations, CERTs (national computer emergency response teams), CIIPs (computer intrusion prevention programs), and cyber security training and awareness. Among forty-eight Asian countries, the study focus on comparative analysis of major neighboring (developed or developing) countries of Pakistan such as Japan, China, Turkey, Iraq, Saudi Arabia, UAE, Nepal, Iran, Malaysia, Jordan, India, and Bangladesh. Cyber security has not been a major concern in Pakistan for a very long time, perhaps due to the way the government operates and how slowly it adapts to new technology. Many Asian countries have only ever released one version of their national cyber security plan, whilst Pakistan has never released anything; some other Asian countries are also added in this group.

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