检索结果-内蒙古大学图书馆

8th International Symposium on Computer science and Intelligent Control, ISCSIC 2024

作者： Yu, Junwei Xia, Yuhe Henan University of Technology School of Artificial Intelligence and Big Data Zhengzhou China Henan University of Technology College of Information Science and Engineering Zhengzhou China

ISBN: (纸本)9798350380286

Skin disease detection has undergone significant advancements with the advent of deep learning-based image segmentation techniques. In this paper, we provide a comprehensive overview of the evolution of skin disease detection, from traditional methods to modernapproaches. We discuss the role of U-Net and its variants in medical image segmentation, highlighting their improved performance and robustness through multi-scale features, multi-task learning, and attention mechanisms. Additionally, we explore the recent trend of generation-based methods, including generative adversarial networks and diffusion models, which have shown promising results in generating high-quality skin disease segmentation masks. We also review popular skin disease segmentation datasets and evaluation metrics,and present experimental results comparing the performance of various models. As deep learning continues to advance and skin disease datasets expand, we anticipate the emergence of innovative network architectures and technologies that will further improve the diagnosis and treatment of skin diseases. This paper aims to inspire future research in this direction, exploring the potential of generation-based methods for skin disease detection and paving the way for more accurate and efficient diagnosis and treatment. © 2024 IEEE.

关键词： Generative adversarial networks

来源：评论

学校读者我要写书评

暂无评论

Conditional selection with CNN augmented transformer for multimodal affective analysis

引用

CAAI Transactions on Intelligence technology 2024年第4期9卷 917-931页

作者： Jianwen Wang Shiping Wang Shunxin Xiao Renjie Lin Mianxiong Dong Wenzhong Guo College of Computer and Data Science Fuzhou UniversityFuzhouChina College of Computer and Cyber Security Fujian Normal UniversityFuzhouChina Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou UniversityFuzhouChina Digital Fujian Institute of Big Data Security Technology Fujian Normal UniversityFuzhouChina Department of Sciences and Informatics Muroran Institute of TechnologyMuroranJapan

Attention mechanism has been a successful method for multimodal affective analysis in recent years. Despite the advances, several significant challenges remain in fusing language and its nonverbal context information. One is to generate sparse attention coefficients associated with acoustic and visual modalities, which helps locate critical emotional se-mantics. The other is fusing complementary cross‐modal representation to construct optimal salient feature combinations of multiple modalities. A Conditional Transformer Fusion Network is proposed to handle these problems. Firstly, the authors equip the transformer module with CNN layers to enhance the detection of subtle signal patterns in nonverbal sequences. Secondly, sentiment words are utilised as context conditions to guide the computation of cross‐modal attention. As a result, the located nonverbal fea-tures are not only salient but also complementary to sentiment words directly. Experi-mental results show that the authors’ method achieves state‐of‐the‐art performance on several multimodal affective analysis datasets.

关键词： affective computing data fusion information fusion multimodal approaches

来源：评论

学校读者我要写书评

暂无评论

Research on Multi-Label Classification of Chinese Text Based on Word-Label Probability and DistilBERT 2

Research on Multi-Label Classification of Chinese Text Based...

引用

2nd IEEE International Conference on Image Processing and Computer Applications, ICIPCA 2024

作者： Zhao, Hong Tian, Weiquan An, Ding Mi, Shan School of Computer Science and Communication Lanzhou University of Technology Lanzhou China Cccc Big Data Technology Co. Ltd. Lanzhou China

ISBN: (纸本)9798350360240

Multi-label text classification is a fundamental task in the field of natural language processing. Currently, there are issues in the Chinese multi-label text classification tasks, such as insufficient extraction of text label features and a lack of learning about the correlations between labels. To address these challenges, we proposed a Multi-label Text Classification Model based on DistilBERT and Word-Label Probabilities (DBWL). The model consists of a metric space module that integrates word-label probabilities and a deep neural network module based on DistilBERT. Firstly, the trained Labeled Latent Dirichlet Allocation (Labeled-LDA) model is utilized to obtain the probabilities of words associated with labels, which are then used to calculate the text mapping vectors and map them into the same metric space as the labels. Similarity measurement is employed to extract features between text-label pairs and label-label pairs. Then, in the deep neural network module, DistilBERT is employed to jointly embed texts and labels, capturing rich semantic information about text labels, followed by feature extraction using TextCNN. Finally, the features extracted from both modules are weighted and fused for label prediction. Experimental results on two benchmark datasets demonstrate that the proposed model outperforms current mainstream multi-label classification models in terms of key performance metrics. © 2024 IEEE.

关键词： Semantics

来源：评论

学校读者我要写书评

暂无评论

AegonKV: A High Bandwidth, Low Tail Latency, and Low Storage Cost KV-Separated LSM Store with SmartSSD-based GC Offloading 23

AegonKV: A High Bandwidth, Low Tail Latency, and Low Storage...

引用

23rd USENIX Conference on File and Storage Technologies, FAST 2025

作者： Duan, Zhuohui Feng, Hao Liu, Haikun Liao, Xiaofei Jin, Hai Li, Bangyu National Engineering Research Center for Big Data Technology and System Service Computing Technology and System Lab/Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology China

ISBN: (纸本)9781939133458

The key-value separation is renowned for its significant mitigation of the write amplification inherent in traditional LSM trees. However, KV separation potentially increases performance overhead in the management of Value region, especially for garbage collection (GC) operation that is used to reduce the redundant space occupation. In response, many efforts have been made to optimize the GC mechanism for KV separation. However, our analysis indicates that such solution based on trade-offs between CPU and I/O overheads cannot simultaneously satisfy the three requirements of KV separated systems in terms of throughput, tail latency, and space usage. This limitation hinders their real-world application. In this paper, we introduce AegonKV, a "three-birds-one-stone" solution that comprehensively enhances the throughput, tail latency, and space usage of KV separated systems. AegonKV first proposes a SmartSSD-based GC offloading mechanism to enable asynchronous GC operations without competing with LSM read/write for bandwidth or CPU. AegonKV leverages offload-friendly data structures and hardware/software execution logic to address the challenges of GC offloading. Experiments demonstrate that AegonKV achieves the largest throughput improvement of 1.28-3.3 times, a significant reduction of 37%-66% in tail latency, and 15%-85% in space overhead compared to existing KV separated systems. © 2025 FAST. All Rights Reserved.

关键词： Digital storage

来源：评论

学校读者我要写书评

暂无评论

mVulPreter: A Multi-Granularity Vulnerability Detection System With Interpretations

引用

IEEE Transactions on Dependable and Secure Computing 2022年 1-12页

作者： Zou, Deqing Hu, Yutao Li, Wenke Wu, Yueming Zhao, Haojun Jin, Hai National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan China School of Computer Science and Engineering Nanyang Technological University Singapore National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China

Due to the powerful automatic feature extraction, deep learning-based vulnerability detection methods have evolved significantly in recent years. However, almost all current work focuses on detecting vulnerabilities at a single granularity (i.e., slice-level or function-level). In practice, slice-level vulnerability detection is fine-grained but may contain incomplete vulnerability details. Function-level vulnerability detection includes full vulnerability semantics but may contain vulnerability-unrelated statements. Meanwhile, they pay more attention to predicting whether the source code is vulnerable and cannot pinpoint which statements are more likely to be vulnerable. In this paper, we design mVulPreter, a multi-granularity vulnerability detector that can provide interpretations of detection results. Specifically, we propose a novel technique to effectively blend the advantages of function-level and slice-level vulnerability detection models and output the detection results' interpretation only by the model itself. We evaluate mVulPreter on a dataset containing 5,310 vulnerable functions and 7,601 non-vulnerable functions. The experimental results indicate that mVulPreter outperforms existing state-of-the-art vulnerability detection approaches (i.e., Checkmarx, FlawFinder, RATS, TokenCNN, StatementLSTM, SySeVR, and Devign). IEEE

关键词： Semantics

来源：评论

学校读者我要写书评

暂无评论

Steel surface Defect Detection Algorithm Based on Improved YOLOv5s 6

Steel surface Defect Detection Algorithm Based on Improved Y...

引用

6th IEEE Advanced Information Management, Communicates, Electronic and Automation Control Conference, IMCEC 2024

作者： Su, Yingying Deng, Yuanyuan Zhou, Nengyang Si, Hongyun Peng, Jie Chongqing University of Science and Technology College of Electrical Engineering Chongqing China Chongqing Key Laboratory of Public Big Data Security Technology Chongqing China

ISBN: (纸本)9798350316520

To address challenges in steel surface defect detection, such as low accuracy and slow processing speed, an enhanced algorithm is proposed. The C3 module is replaced with GSConv (multi-channel shuffle convolution) to improve parameter efficiency and detection accuracy. The nearest neighbor up-sampling is substituted with CARAFE (a lightweight operator) to enhance the receptive field and utilize semantic information. GhostNetV2 replaces the CBS convolutional module for efficient feature extraction through stacking operations. Additionally, the Hard Swish activation function is incorporated to boost accuracy. Experimental results on the NEU-DET dataset demonstrate that the average accuracy of the improved YOLOv5s algorithm is 81.2%, a 1.7% increase compared to the original algorithm, the amount of computation is 17.2GFLOPs and the number of model parameters is 8.65M. The model effectively enhances detection accuracy. © 2024 IEEE.

关键词： Surface defects

来源：评论

学校读者我要写书评

暂无评论

Research on Network Security Access Control Based on Improved Algorithm 24

Research on Network Security Access Control Based on Improve...

引用

9th International Conference on Cyber Security and Information Engineering, ICCSIE 2024

作者： Li, Shenghao Wu, Yuanyuan Chen, Yingsheng School of Big Data and Internet of Things Chongqing Vocational Institute of Engineering China School of Science and Technology Chongqing College of Humanities China

ISBN: (纸本)9798400718137

This article proposes a computer network access isolation control method based on trusted computing. Firstly, the wavelet analysis method is used to analyze computer network access, and then the frequent IP address distance algorithm is used to identify and analyze dangerous access. Based on the characteristics of dangerous access, a network access isolation control model is established using trusted computing technology to complete the overall structural design of the computer network access isolation control system, and analyze the content of each module in the structure. By analyzing the security attributes of network access isolation control, the process of computer network access isolation control is obtained, thereby achieving the establishment and application of a computer network access isolation control system. This method can effectively prevent malicious attacks on computer networks and ensure the safe and stable operation of computer networks. Experimental results have shown that the proposed method can effectively isolate and control computer network access, improve the accuracy of isolation control, reduce the difficulty of computer network access isolation control. © 2024 Copyright held by the owner/author(s).

关键词： Computer network

来源：评论

学校读者我要写书评

暂无评论

Seismology and Climatology: A Study of Seismological Impacts of Climate Change in Indonesia

引用

Atmospheric and Climate sciences 2024年第2期14卷 210-220页

作者： Lukundo Mtambo Xingxiang Tao The Department of Big Data Science Zhejiang University of Science and Technology Hangzhou China

Climate change has been a matter of discourse for the last several decades. Much research has been conducted regarding the causes and impacts of climate change around the world. The current research contributes to the knowledge of the influence of climate change on our environment, with emphasis on earthquake occurrences in the region of Indonesia. Using global temperature anomaly as a measure of climate change, and earthquake data in Indonesia for the period 1900-2022, the paper seeks to find a relationship (if any) between the two variables. Statistical methods used include normal distribution analysis, linear regression and correlation test. The results show peculiar patterns in the progression of earthquake occurrences as well as global temperature anomaly occurring in the same time periods. The findings also indicated that the magnitudes of earthquakes remained unaffected by global temperature anomalies over the years. Nonetheless, there appears to be a potential correlation between temperature anomalies and the frequency of earthquake occurrences. As per the results, an increase in temperature anomaly is associated with a higher frequency of earthquakes.

关键词： Earthquakes Climatology Climate Change Seismology Correlation Linear Regression Indonesia

来源：评论

学校读者我要写书评

暂无评论

SemFlow: Binding Semantic Segmentation and Image Synthesis via Rectified Flow 38

SemFlow: Binding Semantic Segmentation and Image Synthesis v...

引用

38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Wang, Chaoyang Li, Xiangtai Qi, Lu Ding, Henghui Tong, Yunhai Yang, Ming-Hsuan School of Intelligence Science and Technology Peking University China UC Merced United States Institute of Big Data Fudan University China

Semantic segmentation and semantic image synthesis are two representative tasks in visual perception and generation. While existing methods consider them as two distinct tasks, we propose a unified framework (SemFlow) and model them as a pair of reverse problems. Specifically, motivated by rectified flow theory, we train an ordinary differential equation (ODE) model to transport between the distributions of real images and semantic masks. As the training object is symmetric, samples belonging to the two distributions, images and semantic masks, can be effortlessly transferred reversibly. For semantic segmentation, our approach solves the contradiction between the randomness of diffusion outputs and the uniqueness of segmentation results. For image synthesis, we propose a finite perturbation approach to enhance the diversity of generated results without changing the semantic categories. Experiments show that our SemFlow achieves competitive results on semantic segmentation and semantic image synthesis tasks. We hope this simple framework will motivate people to rethink the unification of low-level and high-level vision. © 2024 Neural information processing systems foundation. All rights reserved.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Subpart Suppression Network for Few-Shot Object Counting

Subpart Suppression Network for Few-Shot Object Counting

引用

2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Liu, Lanxin Liu, Xinyan Li, Guorong School of Computer Science and Technology Key Lab of Big Data Mining and Knowledge Management University of Chinese Academy of Sciences China School of Computer Science and Technology Harbin Institute of Technology Weihai China

ISBN: (纸本)9798350368741

Few-shot object counting and detection aim to count objects along with their bounding boxes specified by exemplar bounding boxes. Current mainstream methods predict density maps by applying similarity between exemplar and image features to get counting results and detecting peak points from the density map as the positions of objects. However, the sub-parts of objects can also have high similarity to the exemplars, harming the counting and detection performance. To address these issues, we propose a two-stage Subpart Suppression Network (SSN), consisting of a Subpart Suppression Density map Predictor (SSDP), which enforces the model focus on whole objects rather than subparts, and a SAM-based Detector and Verifier, which refines the final prediction by a clustering method. Extensive experiments on two popular datasets show an advantage in performance over the state-of-the-art methods and prove the components' effectiveness. © 2025 IEEE.

关键词： Counting Detection Few-Shot Localization

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：