检索结果-内蒙古大学图书馆

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

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

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

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

A SUPERVISED INFORMATION ENHANCED MULTI-GRANULARITY CONTRASTIVE LEARNING FRAMEWORK FOR EEG BASED EMOTION RECOGNITION

arXiv

引用

arXiv 2024年

作者： Li, Xiang Song, Jian Zhao, Zhigang Wang, Chunxiao Song, Dawei Hu, Bin 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 Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China Institute of Engineering Medicine Beijing Institute of Technology Beijing China

This study introduces a novel Supervised Info-enhanced Contrastive Learning framework for EEG based Emotion Recognition (SI-CLEER). SI-CLEER employs multi-granularity contrastive learning to create robust EEG contextual representations, potentially improving emotion recognition effectiveness. Unlike existing methods solely guided by classification loss, we propose a joint learning model combining self-supervised contrastive learning loss and supervised classification loss. This model optimizes both loss functions, capturing subtle EEG signal differences specific to emotion detection. Extensive experiments demonstrate SI-CLEER's robustness and superior accuracy on the SEED dataset compared to state-of-the-art methods. Furthermore, we analyze electrode performance, highlighting the significance of central frontal and temporal brain region EEGs in emotion detection. This study offers an universally applicable approach with potential benefits for diverse EEG classification tasks. © 2024, CC0.

关键词： Emotion Recognition

来源：评论

学校读者我要写书评

暂无评论

Parallel Pattern Matching over Brotli Compressed Network Traffic

Parallel Pattern Matching over Brotli Compressed Network Tra...

引用

IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom)

作者： Xiuwen Sun Guangzheng Zhang Di Wu Qingying Yu Jie Cui Hong Zhong School of Computer Science and Technology Anhui University Anhui Provincial Key Laboratory of Network and Information Security Anhui Normal Unviersity

ISBN: (数字)9798350381993

ISBN: (纸本)9798350382006

Pattern matching is a crucial technique for network traffic detection applications. As a fundamental computation model used by pattern matching, the finite state automata execute sequential matching due to the state dependence among transitions. Meanwhile, most services tend to compress their data to improve transmission or storage efficiency. The increased compressed data challenges the straightforward method of matching the whole decompressed data and incurs data dependence among the compression encodings. The related approaches either leverage techniques to break the state dependence of matching uncompressed data or accelerate matching compressed data in a single-threaded manner without considering the state and data dependence. None of them can perform parallel matching over compressed data. This paper provides PETALS, a parallel pattern matching method over Brotli compressed network traffic. PETALS partitions the original compressed traffic into fixed- length blocks for parallel matching and patches the broken compression encodings crossing blocks to break the data dependence. Then, it merges the compressed traffic matching method into path fusion, an enumerative parallelization of finite state automata, to present parallel matching over compressed traffic. Evaluation using real-world network traffic and regular expressions shows that PETALS can raise the speedup from 1.53x to 3.53x of the state-of-the-art parallelization schemes on a 56-cores machine.

关键词： Privacy Instruction sets Computational modeling Automata Telecommunication traffic Programming Parallel processing

来源：评论

学校读者我要写书评

暂无评论

Non-symmetrical Sibling-Stream Network with Adaptive Positional Encoding for Automatic Medical Report Generation

Non-symmetrical Sibling-Stream Network with Adaptive Positio...

引用

Intelligent Medicine and Image Processing (IMIP), International Conference on

作者： Jiayu Si Haifeng Zhao Lili Huang Zhengzheng Tu Anhui Provincial Key Laboratory of Multimodal Cognitive Computation School of Computer Science and Technology Anhui University Hefei Anhui China

Automatic medical report generation technology aims to alleviate the workload of radiologists in writing reports and avoid possible misdiagnosis and missed diagnosis. Recent solutions in medical report generation are mainly based on two mainstream architectures: convolutional neural networks (CNNs) and transformer. However, these two architectures are insufficient for the medical report generation task due to the following three reasons. First, CNN lacks the global context information due to the constrained receptive field of convolution. Second, the complete image is cut into small patches before being fed to the transformer, which leads to the damage to the local details. Third, position encoding is critical to compensate the lost position information caused by serialized input, but the conventional position encoding (e.g., absolute positional encoding) inadequately encodes position information. Therefore, we propose a novel Non-Symmetrical Sibling-Stream Network with Adaptive Positional Encoding which includes three modules: sibling-stream encoder (SSE) module, feature interaction (FI) module, and memory-driven decoder (MDD) module. In detail, in order to cope with respective drawbacks of both architectures, we first propose sibling-stream encoder (SSE) module which combines the global context by transformer and local details (e.g., texture and edge) by CNN. Concurrently, we design a new encoding method named adaptive positional encoding (APE), which can effectively encode the absolute position information within the patch and the relative position information between patches, so as to improve the accuracy of medical report. Then, the feature interaction (FI) module is designed to enhance the image feature representation by interacting the information of sibling-stream. Finally, we introduce a memory-driven decoder to implicitly model and memorize similar patterns in different medical reports during the generation process. Extensive experiments on two public data

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：