检索结果-内蒙古大学图书馆

4th International Conference on computer Vision and Data Mining, ICCVDM 2023

作者： Liu, Bing Zhong, Zhaohao School of Computer Science and Engineering Changchun University of Technology Changchun130012 China

ISBN: (纸本)9781510674448

Semantic segmentation of remote sensing urban scene imagery is a dense prediction task, which has been applied to the land-cover or land-use category. However, the dimension of remote sensing image is huge, which will result in the huge computation cost. In order to reduce the computation cost, a common method is to design a lightweight decoder to achieve a good trade-off between accuracy and computation cost. For this purpose, we design a lightweight transformer-based decoder GDformer. The GDformer consists of our proposed Global Value Transformer and Dynamic feature fusion module. The Global Value Transformer can extract the global semantic feature and the Dynamic feature fusion module can fuse the local feature and global semantic feature dynamic to capture the local-global context with a good trade-off, and the local-global context has been proved is necessary for the semantic segmentation of remote sensing. Extensive experiments prove that our proposed method can achieve a good trade-off between and state-of-the-art performance. © 2024 SPIE.

关键词： Remote sensing

来源：评论

学校读者我要写书评

暂无评论

Survey on Task Scheduling Optimization Strategy under Multi-Cloud Environment

引用

computer Modeling in engineering & Sciences 2023年第6期135卷 1863-1900页

作者： Qiqi Zhang Shaojin Geng Xingjuan Cai School of Computer Science and Technology Taiyuan University of Science and TechnologyTaiyuan030024China College of Electronics and Information Engineering Tongji UniversityShanghai201804China

Cloud computing technology is favored by users because of its strong computing power and convenient *** the same time,scheduling performance has an extremely efficient impact on promoting carbon ***,scheduling research in the multi-cloud environment aims to address the challenges brought by business demands to cloud data centers during peak ***,the scheduling problem has promising application prospects under themulti-cloud *** paper points out that the currently studied scheduling problems in the multi-cloud environment mainly include independent task scheduling and workflow task scheduling based on the dependencies between *** paper reviews the concepts,types,objectives,advantages,challenges,and research status of task scheduling in the multi-cloud *** scheduling strategies proposed in the existing related references are analyzed,discussed,and summarized,including research motivation,optimization algorithm,and related ***,the research status of the two kinds of task scheduling is compared,and several future important research directions of multi-cloud task scheduling are proposed.

关键词： Cloud computing task scheduling workflow review multi-cloud environment

来源：评论

学校读者我要写书评

暂无评论

Q2Cypher: Converting Natural Language Questions to Cypher with Fine-Tuned Large Language Models 5

Q2Cypher: Converting Natural Language Questions to Cypher wi...

引用

5th International Conference on Artificial Intelligence and computer engineering, ICAICE 2024

作者： Zou, Yunqi Wang, Yongli Liu, Dongmei School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China

ISBN: (纸本)9798331528911

Knowledge Base Question Answering (KBQA) intends to obtain credible answers to natural language questions based on knowledge bases. Knowledge graphs, also known as graph databases, explicitly present a large amount of factual knowledge and the associations among them. Neo4j, one of the most popular graph databases, uses the Cypher Query Language (CQL) for efficient querying. Due to its complexity, converting natural language questions into CQL, i.e., Text-to-CQL, is in demand, but the performance is undesirable for existing models. To address the issue, we propose Q2Cypher, a Text-to-CQL framework that is able to generate corresponding CQL based on natural language questions by fine-tuning open-source large language models (LLMs). Q2Cypher employs an unsupervised joint retrieval method to further align entity and relation names in the CQL, thus improving generation from the models more directly. We conduct experiments on open-source LLMs such as ChatGLM2, Baichuan2 and Llama-2, using different fine-tuning methods such as LoRA, Freeze and QLoRA for comparison. The experimental results demonstrate that Q2Cypher is effective and achieves better performance than existing methods on the SpCQL dataset. © 2024 IEEE.

关键词： Knowledge graph

来源：评论

学校读者我要写书评

暂无评论

Detecting Schizophrenia from Healthy control using Machine Learning and EEG Signals 4

Detecting Schizophrenia from Healthy control using Machine L...

引用

4th International Conference on Expert Clouds and Applications, ICOECA 2024

作者： Tamilarasi, K. Pavitra, S. Kumar, Anuj School of Computer Science and Engineering Vellore Institute of Technology Chennai Chennai India

ISBN: (纸本)9798350385793

A brain condition called schizophrenia has a significant impact on people's thinking, behaviors, and lives on a global state. The prevalence of the condition rises along with the population, and those who suffer from schizophrenia lose their capacity for thought and behavior with time. All these issues arise because it is challenging to identify schizophrenia patients. As a result, proper care and quality patient treatment require an accurate and quick diagnosis procedure. This article aims to improve the accuracy of. detection by using EEG signals and Machine Learning algorithms. It is tested using the Resting state EEG dataset, which demonstrates a high level of capability in distinguishing between schizophrenia patients and healthy controls. The EEG data is converted to different forms, where the given machine learning techniques extract the EEG signals to identify patients with schizophrenia and healthy control. We are utilizing the 28-file public dataset in this study. In the file, there are 14 healthy individuals as well as 14 SCZ sufferers. In accordance with the findings, (1) the decision tree successfully distinguished SCZ patients from healthy individuals using EEG signals with an accuracy of 89.12%, and (2) logistic regression was also used to successfully identify the affected patient using EEG signals with an accuracy of 69.78%. (3) The SCZ patient was correctly identified by SVM with an accuracy of 65.51 %. Our research demonstrates the potential for machine learning to play a significant role in aiding in the diagnosis of schizophrenia. © 2024 IEEE.

关键词： Logistic regression

来源：评论

学校读者我要写书评

暂无评论

Enhanced Secure Communication in Wireless Sensor Networks: Quantum Key Distribution with KIRSAN Gate 5

Enhanced Secure Communication in Wireless Sensor Networks: Q...

引用

5th International Conference on Electronics and Sustainable Communication Systems, ICESC 2024

作者： Manivannan, Kirankumar Vivekanandan, M. Saranya, G. Kumaran, K. School of Electronics Engineering Vellore Institute of Technology Chennai campus India School of Computer Science and Engineering Vellore Institute of Technology Chennai campus India

ISBN: (纸本)9798350379945

Sensor nodes are integral to wireless sensor networks (WSNs) but their deployment across various locations exposes their communications to potential monitoring. Key challenges include message protection and node identification, necessitating efficient mechanisms for key distribution and management, especially in large-scale networks. Quantum cryptography, specifically quantum key distribution (QKD), offers a method to securely establish keys over short distances, leveraging principles like garbage output reduction and quantum communication for enhanced security. To address these issues, reversible logic gates, such as the newly developed © 2024 IEEE.

关键词： Quantum cryptography

来源：评论

学校读者我要写书评

暂无评论

Improved UNet for Semi-Supervised Multi-Scale CMRI Right Ventricular Segmentation Method 9

Improved UNet for Semi-Supervised Multi-Scale CMRI Right Ven...

引用

9th International Conference on Intelligent Computing and Signal Processing, ICSP 2024

作者： Cui, Hongzhen Wang, Shichao Zhu, Xiaoyue Peng, Yunfeng School of Computer and Communication Engineering University of Science and Technology Beijing Beijing China School of Electronics and Information Engineering Liaoning University of Technology Liaoning China

ISBN: (纸本)9798350376548

Segmentation of the right ventricle (RV) holds significant clinical implications for the assessment of cardiac function in conditions such as pulmonary hypertension. Precise delineation of the right ventricle from cardiac magnetic resonance images (CMRI) is crucial in the diagnosis of various cardiac conditions. It provides clinicians with essential information for patient evaluation, treatment planning, and subsequent rehabilitation strategies. Currently, the development of fully automated segmentation models for the right ventricle heavily relies on deep learning techniques, necessitating meticulous manual annotation of CMRI data to achieve optimal segmentation results. However, the labor-intensive nature of labeling CMRI data requires significant medical expertise and consumes considerable time and resources. In response to these challenges, this study proposes a semi-supervised multi-scale segmentation approach for the right ventricle in CMRI. The method leverages unlabeled data to enhance segmentation accuracy while minimizing the reliance on annotated data. Experimental findings demonstrate the effectiveness of the proposed method in accurately delineating the right ventricle from CMRI datasets. © 2024 IEEE.

关键词： Magnetic resonance imaging

来源：评论

学校读者我要写书评

暂无评论

CauImputation: Utilizing Structural Causal Model in Data Imputation

CauImputation: Utilizing Structural Causal Model in Data Imp...

引用

2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Yin, Yue Yang, Jiaoyun An, Ning Hefei University of Technology School of Computer Science and Information Engineering Hefei China

ISBN: (纸本)9798350386226

Missing data pose significant obstacles in data analysis. Many imputation methods, operating under the assumption that similar instances exhibit similar feature values, often overlook the essential role of the feature containing the missing value. These methods tend to apply a uniform strategy to all missing data within a single instance. This study proposes selecting neighbor instances for imputation based on both instance and missing feature information. To support this approach, a Structural Causal Model (SCM) delineates the relationships among missing data, features, and instances. The model reveals that the feature with the target missing value acts as a confounder, influencing both the selection of neighbor instances and the prediction of the missing value. Building upon these findings, this study introduces a new imputation method called the Causal Imputation strategy (CauImputation). CauImputation utilizes both feature and instance information to select neighbor instances and then applies causal interventions to compute the missing value. This strategy effectively leverages feature information while mitigating confounding effects. Tests on the National Alzheimer's Coordinating Center (NACC) dataset and the National Center for Biotechnology Information (NCBI) microarray datasets demonstrate that CauImputation outperforms nine established methods, achieving a 3% to 15% increase in imputation accuracy and reducing root mean squared errors by 0.05 to 0.2. By utilizing the Structural Causal Model, this research introduces a novel perspective on data imputation, enhancing the robustness and reliability of biomedical data analyses. © 2024 IEEE.

关键词： Data accuracy

来源：评论

学校读者我要写书评

暂无评论

Translation for Hindi-English: A Comparative Performance Analysis of Multilingual and Code-Mixed Text 10

Translation for Hindi-English: A Comparative Performance Ana...

引用

10th International Conference on Advanced Computing and Communication Systems, ICACCS 2024

作者： Afsal, C.P. Kuppusamy, K.S. School of Engineering and Technology Pondicherry University Department of Computer Science Puducherry India

ISBN: (数字)9798350384369

ISBN: (纸本)9798350384369

The importance of code-mixed text is significant in light of India's rich diverse languages and cultures. Its significance is particularly notable in this modern era, as people are spending more and more time on social media platforms. Here, people use code-mixed text for convenience in conversations. Therefore, it's crucial to focus research efforts on these important topics. In this study, we aim to investigate the comparative performance of machine translation from Hindi to English and Hindi-English code-mixed text to English. We used a publicly available dataset HinGE introduced by Srivastava et al. Due to the relatively small size of our dataset, we opted for the MarianMT model, a transformer-based architecture that has been recognized for its ability to perform machine translation tasks, even when dealing with limited data. The BLEU scores of 0.91 for multilingual translation and a remarkable 0.97 for code-mixed Hindi-English translation indicate that our choice performed well. These findings highlight the model's excellent results and ability to perform in situations with limited training data. © 2024 IEEE.

关键词： Machine translation

来源：评论

学校读者我要写书评

暂无评论

Supervised Data-Dependent Kernel Low-Rank Preserving Projection for Image Recognition 5

Supervised Data-Dependent Kernel Low-Rank Preserving Project...

引用

5th International Conference on Intelligent Computing and Human-computer Interaction, ICHCI 2024

作者： Li, Linhao Zhang, Ao Jiangsu University of Science and Technology School of Computer Science and Engineering Zhenjiang China

ISBN: (纸本)9798350368284

Dimensionality reduction (DR) is a frequently used method to handle data with many dimensions. This article presents a novel approach to nonlinear dimensionality reduction, referred to as supervised data-dependent kernel low-rank preserving projection (SDKLRPP). Designed for image recognition tasks. The method enhances performance by utilizing a data-dependent kernel (DK). Unlike traditional nonlinear dimensionality reduction techniques, the SDKLRPP method described in this paper can fine-Tune the coefficients within the DK and uncover The paper elucidates the low-rank reconstructive relationship inherent in the data. It also illustrates how the manifold structure within the feature space is mirrored in the label space, grounded in the principle of smoothness. This enables label information to direct the optimization of coefficients within the DK, thereby enhancing the capacity to discern among various classes. Upon attaining the optimal low-rank reconstructive relationship, a transformation matrix is computed to maintain this relationship, which is subsequently utilized to project the data into a reduced-dimensional space. The efficacy of the proposed SDKLRPP methodology is substantiated through experiments conducted on five extensively acknowledged databases. © 2024 IEEE.

关键词： Dimensionality reduction

来源：评论

学校读者我要写书评

暂无评论

A Lightweight Reverse Distillation for Texture Anomaly Detection 9

A Lightweight Reverse Distillation for Texture Anomaly Detec...

引用

9th International Conference on Intelligent Informatics and BioMedical Sciences, ICIIBMS 2024

作者： Lu, Chang Li, Wenju Wang, Xubin Shanghai Institute of Technology School of Computer Science and Information Engineering Shanghai China

ISBN: (数字)9798350363043

ISBN: (纸本)9798350363043

Reverse Distillation (RD) has achieved effective results in the challenge of unsupervised anomaly detection on the textured surfaces of industrial products. However, original RD has very large numbers of parameters, making it unsuitable for practical deployment. To address this issue, we proposed a lightweight reverse distillation called RD-Lite. Efforts to reduce the model's weight were made in two parts. First, the depth and the number of feature channels of the model were significantly reduced. Additionally, the student network and the bottleneck layer were reconstructed using depthwise separable convolution modules. The streamlined model maintains excellent anomaly detection performance while keeping the model size at a tiny level. Experiments on benchmark texture datasets show that our RDLite achieved a sample-level AUROC of 98.92%, which is very close to the accuracy of the original RD, while the number of our model's parameters is only 3.3% of the original one. © 2024 IEEE.

关键词： Anomaly detection

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：