检索结果-内蒙古大学图书馆

International Conference on Networking and Network Applications (NaNA)

作者： Lei Huang Huiling Shi Wei Zhang Kun Yu Shandong Provincial Key Laboratory of Computer Networks Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China College of Computer Jingchu University of Technology Jingmen China

Emerging network applications, such as virtual reality technology, autonomous driving, and large-scale Internet of Things, usually require a significant amount of bandwidth and have high requirements for data delivery time. However, the existing Quick UDP Internet Connection (QUIC), TCP, and other protocols cannot fully meet these needs. A scheduling algorithm based on a time-sensitive protocol can meet the above requirements at the transport layer and reduce the development difficulty of the application layer. Therefore, this paper is based on Deadline-aware Transport Protocol (DTP) and implemented in a Python 3.0 environment. It focuses on the scenario where video data blocks are sent to the transport layer. In this paper, we study the decision-making method and congestion control method for data block priority and deadline, and optimize the delay-sensitive transmission protocol. We propose a scheduling algorithm to meet the service's delay demands and enhance the overall service quality. Additionally, we introduce an improved scheduling scheme for network scenarios involving multiple senders or receivers. Our research aims to effectively address the service delay requirements under different network conditions.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Time series classification based on Unet with 2D transformation

Time series classification based on Unet with 2D transformat...

引用

International Conference on Identification, Information and Knowledge in the Internet of Things (IIKI)

作者： Mingsen Du Yunxia Wang Yanxuan Wei Xiaona Wu Cun Ji School of Information Science and Engineering Shandong Normal University Jinan China Shandong Provincial Key Laboratory for Distributed Computer Software Novel Technology Jinan China

ISBN: (数字)9798331510633

ISBN: (纸本)9798331510640

Time series classification has gained significant attention in the data mining field in recent years. Despite the proposal of numerous methods over the past decades, most of these approaches focus on feature extraction in one-dimensional space, which often results in lower classification accuracy. To address this issue, this paper proposes a Time Series Classification method based on U-Net with 2D transformation (TSC-Unet). This method first transforms univariate time series into 2D space and augments the data, effectively enhancing the diversity of the dataset. Subsequently, features are extracted from the 2D space using a fully convolutional network, fully exploiting multi-scale feature information. U-Netwith skip connections, effectively retains and transmits high-resolution features, significantly reducing information loss and improving segmentation performance. Additionally, the ability to learn multi-scale features enables the model to capture details at different levels, thereby enhancing its understanding of complex data. Experimental results demonstrate that TSC-Unet achieves high classification accuracy on the UCR datasets, validating the effectiveness and superiority of the proposed method.

关键词： Accuracy Time series analysis Transforms Feature extraction Propagation losses Data models Data mining Convolutional neural networks Proposals Internet of Things

来源：评论

学校读者我要写书评

暂无评论

An Artificial Intelligence-Based Segmentation Auxiliary Diagnostic System for Focal Liver Lesions

An Artificial Intelligence-Based Segmentation Auxiliary Diag...

引用

Mechatronics, IoT and Industrial Informatics (ICMIII), International Conference on

作者： Jingjun Gu Zhi Yu Wei Zhang Haoyu Hu Zhejiang Provincial Key Laboratory of Service Robot College of Computer Science Zhejiang University Hangzhou China School of Software Technology Zhejiang University Hangzhou China

ISBN: (数字)9798350386639

ISBN: (纸本)9798350386646

In this paper, we introduce an artificial intelligence-based system designed for the segmentation and auxiliary diagnosis of focal liver lesions. This system can effectively segment lesions in both singlephase non-contrast CT images and multiphase contrast-enhanced CT images. Additionally, it has the capability to synchronize CT data from Picture Archiving and Communication Systems (PACS) and to visualize the segmentation results. Our AI system aims to meet the demand for rapid screening of liver lesions in primary hospitals and physical examination institutions, while also fulfilling the more precise segmentation needs of provincial and municipal hospitals.

关键词： Image segmentation Hospitals Computed tomography Scalability Liver Data visualization Picture archiving and communication systems

来源：评论

学校读者我要写书评

暂无评论

ERC DMSP: Emotion Recognition in Conversation Based on Dynamic Modeling of Speaker Personalities

ERC DMSP: Emotion Recognition in Conversation Based on Dynam...

引用

International Joint Conference on Neural networks (IJCNN)

作者： Xiaoyang Li Zhenyu Yang Zhijun Li Yiwen Li 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) Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

Emotion Recognition in Conversations (ERC) is a widely applicable task, whether in emotional chatbots or recommender systems for dialog scenarios. The ability of systems to accurately identify human emotions is a crucial component in ERC success. In everyday conversations, a speaker’s emotional expression is closely tied to their individual characteristics and personality traits. Furthermore, individuals exhibit significant variations in their expressions of the same emotion. Traditional methods for ERC often overlook these personality differences and dynamic changes, leading to unreliable results. To address this issue, we propose a conversation emotion recognition model based on dynamic modeling of speaker’s personality (ERC DMSP). In our approach, to extract a speaker’s personality profile, we design a personality capture module to model the personalities of individuals based on their historical utterances. Moreover, a bidirectional influence exists between a speaker’s personality tendencies and their linguistic behavior. The speaker’s personality profile is thus continually updated based on the ongoing dialog. Additionally, since the personalities of other speakers in the conversation can also affect the form and intensity of a speaker’s emotional expression, we construct a conversation emotion representation utilizing group personality traits. We then comprehensively incorporate the speaker’s personality into the emotion representation of each utterance at both the group and individual levels through the personality engagement module. Throughout this process, the representation of dialog and the speaker’s personality undergo hierarchical updates, leading to more accurate conversation emotion recognition. We conducted extensive experiments on four commonly used public benchmark datasets for ERC to evaluate the effectiveness of our proposed ERC DMSP model. The results demonstrate its efficacy, as it exhibits a significant improvement over other methods in popular

关键词： Measurement Emotion recognition Accuracy Codes Neural networks Oral communication Color

来源：评论

学校读者我要写书评

暂无评论

Ajsage: A Intrusion Detection Scheme Based on Jump-Knowledge Connection to Graphsage

SSRN

引用

SSRN 2024年

作者： Xu, Lijuan Zhao, ZiCheng Zhao, Dawei Li, Xin Lu, XiYu Yan, DingYu Qilu University of Technology Shandong Academy of Sciences Jinan250014 China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan250014 China National Computer Network Emergency Response Technology Processing Coordination Center Jinan250014 China

In the field of network security, attackers often utilize Advanced Persistent Threats (APT) to conduct host-based intrusions for prolonged information gathering, penetration and to cause serious damages. Recent studies have used provenance data containing rich contextual information to achieve effective detection of host-based APT. Extracting system entities (e.g., processes, files) and operations between entities in provenance data to construct a directed acyclic graph (DAG) is the key to realize attack detection by provenance graph. Previous studies extracted the features of the whole provenance graph, which did not fully capture the relationship between the nodes in the graph, and the extracted features were not accurate enough. Moreover, the original node feature information may be lost in the process of aggregation. Therefore, abnormal nodes are recognized in the detection process, leading to low detection performance and a high false alarm rate. Facing the challenge, we introduce AJSAGE, a framework based on graph neural networks. A novel anomaly detection method by adding attention mechanism and Jump-Knowledge Connection to GraphSAGE. It enables the integration of node information across hierarchical levels, improves the detection of complex attack patterns, and enhances the accuracy and generalization of the model in node feature representation. It is able to identify features and nodes that are closely related to the anomaly detection task in a more focused manner. We evaluate the performance of AJSAGE on three publicly available datasets, and the results demonstrate that it significantly outperforms multiple state-of-the-art methods for host intrusion detection. © 2024, The Authors. All rights reserved.

关键词： Intrusion detection

来源：评论

学校读者我要写书评

暂无评论

Knowledge tracking model based on recurrent neural network and transformer 8

Knowledge tracking model based on recurrent neural network a...

引用

8th International Symposium on Advances in Electrical, Electronics, and computer Engineering, ISAEECE 2023

作者： Cheng, Yan Zhao, Songhua Hu, Jiansheng Zou, Haifeng Luo, Pin Fu, Yan Zhong, Linhui Liu, Chunlei School of Computer and Information Engineering Jiangxi Normal University Nanchang330022 China Jiangxi Province Science and Technology Innovation Base-Provincial Key Laboratory "Jiangxi Province Key Laboratory of Intelligent Education" Nangchang330022 China Jiangxi Ruanyun Technology Corporation Limited Nanchang330096 China Jiangxi Heyi Keji Youxian Gongsi Nanchang330096 China

ISBN: (纸本)9781510666290

With the continuous development of online education platform, knowledge tracking (KT) has become a key technology to help online education platform provide personalized education. However, the existing knowledge tracking model based on recurrent neural network is difficult to be used for the input of long sequence, and has the problem of long-term dependence. Secondly, although the knowledge tracking model based on Transformer does not have the problem of long-term dependence, it is difficult to capture the input sequence information. Therefore, this paper proposes a knowledge tracking model based on recurrent neural network and transformer. A new position coding method is designed, and LSTM is used to replace the position coding method of Transformer to encode sequence features, so that the model in this paper can not only capture the input sequence information, but also get rid of the long-term dependency problem based on the recurrent neural network, and use GRU network to capture the context information. In addition, an adaptive fusion gate is designed to fuse the global features and context features obtained by Transformer, and use the fused features to predict the students' answers to the next question. In addition, an adaptive fusion gate is designed to fuse the global features and context features obtained by Transformer, and use the fused features to predict the students' answers to the next question. © 2023 SPIE. All rights reserved.

关键词： Students

来源：评论

学校读者我要写书评

暂无评论

A High-Concentration Edge-Nitrogen-Doped Porous Carbon Anode via Template Free Strategy for High-Performance Potassium-Ion Hybrid Capacitors

引用

Energy Material Advances 2024年第1期5卷 517-526页

作者： Zhen Pan Ke Li Lidong Sun Yang Li Zuodong Zhang Yitai Qian Wei Chen Department of Applied Chemistry School of Chemistry and Materials ScienceHefei National Research Center for Physical Sciences at the MicroscaleUniversity of Science and Technology of ChinaHefeiChina Key Laboratory of Agricultural Sensors Ministry of Agriculture and Rural AffairsAnhui Provincial Key Laboratory of Smart Agricultural Technology and EquipmentSchool of Information and ComputerAnhui Agricultural UniversityHefeiChina

Developing facile and economical strategies to fabricate nitrogen-doped porous carbon anode is desirable for dual-carbon potassium ion hybrid capacitors(PIHCs).Here,a high-concentration edge-nitrogen-doped porous carbon(NPC)anode is synthesized by a template-free strategy,in which the total content of pyrrolic nitrogen and pyridinic nitrogen accounts for more than 80%of the nitrogen *** a result,the NPC anode displays a capacity of 315.4 mA h g^(−1)at a current rate of 0.1 A g^(−1)and 189.1 mA h g^(−1)at 5 A g^(−1).Ex situ characterizations and density functional theory calculations demonstrate the high-concentration edge-nitrogen doping enhances K^(+)adsorption and electronic conductivity of carbon materials,resulting in good electrochemical *** assembled NPC//CMK-3 PIHC delivers an energy density of 71.1 W h kg^(−1)at a power density of 771.9 W kg^(−1)over 8,000 cycles.

关键词： potassium ion hybrid capacitors pihcs herea template free strategy pyrrolic nitrogen pyridinic nitrogen high concentration edge nitrogen doped porous carbon porous carbon anode density functional theory calculations potassium ion hybrid capacitors

来源：评论

学校读者我要写书评

暂无评论

Rafmnet:Reinforced Attention Fusion and Multiscale Network For Noisy Infrared and Visible Image Fusion

Rafmnet:Reinforced Attention Fusion and Multiscale Network F...

引用

IEEE International Conference on Image Processing

作者： Guohua Lv Xiyan Wang Yongbiao Gao Yi Zhai Guixin Zhao Guangxiao Ma 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) Jinan China Faculty of Comuter Science and Technology Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China College of Computer Science and Engineering Shandong University of Science and Technology Qingdao China

ISBN: (数字)9798350349399

ISBN: (纸本)9798350349405

The purpose of infrared and visible image fusion is to combine the advantages of different types of images to produce more robust and informative images. However, if the source images are noisy, existing fusion methods may not produce clear results. To address this issue, we propose a novel method for infrared and visible image fusion with noise reduction. This method enhances the visual perception of fused images by integrating features of different scales extracted by the denoising network into the fusion network. By using deformable convolutional denoising networks, noise in images can be removed and features can be enhanced. Then, a set of reinforced attention fusion modules (RAFM) are designed to fuse the features extracted by the denoising network. Experimental results demonstrate the effectiveness of our proposed method, which outperforms existing state-of-the-art methods in terms of fusion accuracy and visual perception.

关键词： Couplings Fuses Noise reduction Noise Feature extraction Noise measurement Task analysis

来源：评论

学校读者我要写书评

暂无评论

Deep learning-based Image-Like Channelization for Broadband Receiver 2

Deep learning-based Image-Like Channelization for Broadband ...

引用

Advanced Optical Manufacturing Technologies and Applications 2022, AOMTA 2022 and 2nd International Forum of Young Scientists on Advanced Optical Manufacturing, YSAOM 2022

作者： Huang, Yu Zhang, Jiejun Yao, Jianping Guangdong Provincial Key Laboratory of Optical Fiber Sensing and Communications Institute of Photonics Technology Jinan University Guangzhou511443 China Microwave Photonics Research Laboratory School of Electrical Engineering and Computer Science University of Ottawa OttawaONK1N 6N5 Canada

ISBN: (纸本)9781510661264

We propose a novel image-like channelization method that utilizes a convolutional recurrent neural network (CRNN) for channel synthesis to reduce the bandwidth requirements of the electrical hardware. In this study, the spectrum of a 30-GBaud QPSK signal is spectrally sliced and received by four low-speed coherent receivers based on a conventional coherent optical communication system. After the recovery of the trained CRNN, the average error vector magnitude (EVM) of the 30-GBaud baseband signal is improved from over 60% by uncorrected channel synthesis to around 15%. © 2023 SPIE.

关键词： Recurrent neural networks

来源：评论

学校读者我要写书评

暂无评论

A Job-Aware Decision Method for Hybrid HPC Cluster Scenarios

A Job-Aware Decision Method for Hybrid HPC Cluster Scenarios

引用

6th International Conference on Intelligent Computing and Signal Processing (ICSP)

作者： Yang Bai Ying Guo Hu Zhang Jibin Wang Jing Chen Shandong Computer Science Center(National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Jinan China

As an emerging science and technology industry, the development direction and strategic height of high-performance computing has become one of the important symbols of scientific and technological progress nowadays, and also a centralized embodiment of a country's comprehensive strength in science and technology. To cope with the development demand of high performance computing, meeting the resource utilization conditions under multi-dimensional constraints and forming reasonable scheduling are always the most urgent issues. Based on the current development status of hybrid HPC clusters and the problem of resource wastage due to unreasonable scheduling allocation, a job task-aware migration policy method applicable to hybrid HPC cluster environment is proposed in this paper: ECP - Perception-aware decision method, ECP - Perception-aware decision method can target HPC job tasks submitted by users The ECP - Perception-aware decision method can make reasonable load prediction for HPC job tasks submitted by users, and migrate the job tasks to a cluster environment with relatively lower resource consumption or monetary cost based on the prediction result and the perception policy. After comparing several different job task migration models in simulated production environments, the results show that the ECP - Perception-aware decision approach enables HPC job tasks to obtain lower resource consumption or lower monetary cost in a hybrid HPC cluster environment than when running in a single physical HPC cluster or Kubernetes cluster, and also enables the hybrid HPC cluster to resources as well as users' demands are better balanced before, which better solves the multi-objective optimization problem of HPC job task-aware scheduling.

关键词：

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：