检索结果-内蒙古大学图书馆

REAL-TIME GLUCOSE MONITORING SYSTEM AND DIETARY RECOMMENDATIONS FOR DIABETES MELLITUS USING FOG COMPUTING

Telecommunications and Radio Engineering (English translation of Elektrosvyaz and Radiotekhnika) 2025年第5期84卷 55-67页

作者： Sathish, N. Elangovan, D. Nagalakshmi, R. Suresh, G. Chitra, Devi D. Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India Department of Information Technology Saveetha Engineering College Chennai India Department of Computer Science and Engineering SRM Institute of Science and Technology Ramapuram Chennai India Department of Artificial Intelligence and Data Science Kings Engineering College Sriperumbudur India Department of Computer Science And Engineering S.A. Engineering College Chennai India

This systematic review gave special attention to diabetes and the advancements in food and nutrition needed to prevent or manage diabetes in all its forms. There are two main forms of diabetes mellitus: Type 1 (T1D) and Type 2 (T2D). The loss of beta cells brought on by an autoimmune reaction causes the pancreas to be unable to produce enough insulin, which leads to Type 1 diabetes. A deficiency of insulin could arise as the illness worsens. The most frequent cause is a combination of excessive body weight and inadequate exercise. Currently, 90–95% of patients have Type 2 diabetes mellitus (T2DM). Age-specific normal blood sugar ranges exist and knowing them is crucial for determining a target blood sugar level that is both healthy and appropriate. The main takeaway is that diet regimens ought to be tailored to the patient’s requirements and should account for their capacity for change. Carbohydrates are the nutrients that have the biggest impact on the rise in blood glucose. In this paper, we have proposed a novel method for glucose monitoring and diet recommendations for diabetic patients using fog computing. A paradigm known as fog computing makes use of the benefits that come from using both cloud and edge devices to support modern computing systems by offering high-quality services, cutting latency, facilitating mobility, supporting multi-tenancy, and many other features. © 2025 by Begell House, Inc.

关键词： Fog computing

来源：评论

学校读者我要写书评

暂无评论

Ince-EEGNet: A Novel Fatigue Degree Detection Method Based on EEG 17

Ince-EEGNet: A Novel Fatigue Degree Detection Method Based o...

引用

17th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, CISP-BMEI 2024

作者： Ke, Jingshuang Gao, Ruochen Huang, Zhihua College of Computer and Data Science Fuzhou University Fuzhou China

ISBN: (纸本)9798331507398

In this paper, we proposed a convolutional neural network based on EEGNet, which can be used to classify the degree of fatigue, without manual feature extraction, and directly take the original EEG as input, and the results of five-fold cross-validation show that the performance of the network is better than that of shallowConvNet and EEGNet, with the highest accuracy of 98.02%. Using EEG samples of different frequency bands as model inputs, it was found that the sample accuracy of gamma rhythm and beta rhythm was higher, both exceeding 90 %, and the accuracy of delta rhythm and theta rhythm was lower, only about 50 %. In addition, the frontal lobe had the highest classification accuracy in all brain regions, reaching nearly 94%, and the left brain had an accuracy of about 4 % higher than that of the right brain. © 2024 IEEE.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

Capturing Minds, Not Just Words: Enhancing Role-Playing Language Models with Personality-Indicative data

Capturing Minds, Not Just Words: Enhancing Role-Playing Lang...

引用

2024 Conference on Empirical Methods in Natural Language Processing, EMNLP 2024

作者： Ran, Yiting Wang, Xintao Xu, Rui Yuan, Xinfeng Liang, Jiaqing Yang, Deqing Xiao, Yanghua School of Data Science Fudan University China School of Computer Science Fudan University China

ISBN: (纸本)9798891761681

Role-playing agents (RPA) have been a popular application area for large language models (LLMs), attracting significant interest from both industry and academia. While existing RPAs well portray the characters' knowledge and tones, they face challenges in capturing their minds, especially for small role-playing language models (RPLMs). In this paper, we propose to enhance RPLMs via personality-indicative data. Specifically, we leverage questions from psychological scales and distill advanced RPAs to generate dialogues that grasp the minds of characters. Experimental results validate that RPLMs trained with our dataset exhibit advanced role-playing capabilities for both general and personality-related evaluations. Code and data are available at https://***/alienet1109/RolePersonality. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

来源：评论

学校读者我要写书评

暂无评论

Community Detection with Deep Attention Autoencoder Based on Subspace Constraints 4

Community Detection with Deep Attention Autoencoder Based on...

引用

4th International Conference on Neural Networks, Information and Communication Engineering, NNICE 2024

作者： Cai, Ziqi Chen, Jianguo Wu, Ling Fuzhou University College of Computer and Data Science Fuzhou China

ISBN: (纸本)9798350394375

Subspace clustering, known for its effectiveness in handling high-dimensional data, has attracted attention. And the autoencoder can discover hidden features within a large dataset, yet it faces challenges in utilizing attribute information for reconstruction and capturing complex spatial structural information. To tackle these issues, we propose a community detection algorithm named Deep Attention Autoencoder Based on Subspace Constraints (DAASC). First, we design an attribute topology fusion strategy to integrate attribute information into the reconstruction of the decoder. Then, we design a subspace autoencoder strategy, using the concept of subspaces to construct the loss function, to capture the spatial structural information of the data. Experiments conducted on both real-world and synthetic networks to compare DAASC with several state-of-the-art community detection algorithms demonstrate its exceptional accuracy and robustness. © 2024 IEEE.

关键词： Complex networks

来源：评论

学校读者我要写书评

暂无评论

data Constraints? Not anymore: Image-Only Learning with Cross-Institutional Applicability for Ultrasound Video Segmentation

Data Constraints? Not anymore: Image-Only Learning with Cros...

引用

2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Yan, Zejie Yu, Chunyan College of Computer and Data Science Fuzhou University Fuzhou China

ISBN: (纸本)9798350386226

In video analysis, existing approaches achieve excellent performance under preset conditions, but the scarcity of training videos limits deep learning development. To address this, this paper introduces two external resources to alleviate the video shortage: image data and cross-institutional data. We propose a novel method, IOLCIA, which combines image-only learning and cross-institutional learning to maximize the use of image resources. Additionally, it leverages the differences and similarities in data from various institutions to enhance feature learning. Comparison experiments show that our method outperforms models trained solely on images, with only a slight decrease in effectiveness compared to those trained on videos. © 2024 IEEE.

关键词： Video analysis

来源：评论

学校读者我要写书评

暂无评论

An Adversarial Text Generation Framework Based on Multi-View and Extended Semantic Space 7

An Adversarial Text Generation Framework Based on Multi-View...

引用

7th International Conference on Advanced Algorithms and Control Engineering, ICAACE 2024

作者： Du, Xue Lin, Xuanwei Liu, Ximeng College Of Computer And Data Science Fuzhou University Fuzhou China

ISBN: (纸本)9798350361445

Adversarial attack in the black-box scenario have become a highly threatening security issue in natural language processing tasks such as text classification and sentiment analysis. Unlike continuous images, text space is discrete, and attack cannot be achieved by directly perturbing the input, making it challenging to generate adversarial texts. The current adversarial attack methods are mainly based on the heuristic single-path strategy by finding the synonyms of some tokens in the text and replacing them. However, these methods tend to generate adversarial examples with limited attack performance, weak semantic preservation, and drastic fluency change. This paper innovatively proposes an edit-based adversarial text generation method to trade-off between test accuracy decrease and stealthiness for generated adversarial texts. Extensive experimental results demonstrate that our method outperforms baselines. Our AdvGen algorithm achieved a 2.5% improvement in attack effectiveness on the IMDB dataset. At the same time, our method reduces the Levenshtein distance by 5.9%, increases the semantic similarity by 2.5% on the AG dataset, and has an overall lower perplexity on all datasets. © 2024 IEEE.

关键词： Sentiment analysis

来源：评论

学校读者我要写书评

暂无评论

Hypergraph Representation Learning from Noisy Node Attributes 14

Hypergraph Representation Learning from Noisy Node Attribute...

引用

14th International Conference on Information science and Technology, ICIST 2024

作者： Tang, Qian-Xi Zhang, Chun-Yang College of Computer and Data Science Fuzhou University Fuzhou China

ISBN: (纸本)9798350353334

In recent years, hypergraph representation learning (HGRL) has become a focus of academic research, which aims to extract high-order topological patterns and attributes from hypergraph into low-dimensional representation vectors. However, most existing methods ignore the uncertainties in hypergraph data, thus failing to effectively leverage attribute features hidden in hypergraphs. For example, in a citation hypergraph, there exist uncertain semantics in node attributes regarding to papers. Therefore, we propose a new Fuzzy HGRL model, called FAHGN, which introduces fuzzy logic to grasp the attribute uncertainties. Specifically, the proposed FAHGN fuzzifies node attributes of the hypergraph as fuzzy input hypergraph signals, and makes full use of a spectral graph convolution operator to aggregate the fuzzy input signals to generate node representations. Through this way, it effectively considers feature-level uncertainties in hypergraphs and provides more expressive representations for effectively achieving downstream tasks. The experimental results on five real-world datasets validate the effectiveness of the proposed FAHGN against competitive baseline models. © 2024 IEEE.

关键词： Semantics

来源：评论

学校读者我要写书评

暂无评论

Prompting Towards Alleviating Code-Switched data Scarcity in Under-Resourced Languages with GPT as a Pivot 3

Prompting Towards Alleviating Code-Switched Data Scarcity in...

引用

3rd Annual Meeting of the ELRA-ISCA Special Interest Group on Under-Resourced Languages, SIGUL 2024

作者： Terblanche, Michelle Olaleye, Kayode Marivate, Vukosi Data Science for Social Impact Dept. of Computer Science University of Pretoria South Africa

ISBN: (纸本)9782493814296

Many multilingual communities, including numerous in Africa, frequently engage in code-switching during conversations. This behaviour stresses the need for natural language processing technologies adept at processing code-switched text. However, data scarcity, particularly in African languages, poses a significant challenge, as many are low-resourced and under-represented. In this study, we prompted GPT 3.5 to generate Afrikaans–English and Yoruba–English code-switched sentences, enhancing diversity using topic-keyword pairs, linguistic guidelines, and few-shot examples. Our findings indicate that the quality of generated sentences for languages using non-Latin scripts, like Yoruba, is considerably lower when compared with the high Afrikaans–English success rate. There is therefore a notable opportunity to refine prompting guidelines to yield sentences suitable for the fine-tuning of language models. We propose a framework for augmenting the diversity of synthetically generated code-switched data using GPT and propose leveraging this technology to mitigate data scarcity in low-resourced languages, underscoring the essential role of native speakers in this process. © 2024 ELRA Language Resource Association.

关键词： Natural language processing systems

来源：评论

学校读者我要写书评

暂无评论

Identifying Affected Libraries and Their Ecosystems for Open Source Software Vulnerabilities 24

Identifying Affected Libraries and Their Ecosystems for Open...

引用

44th ACM/IEEE International Conference on Software Engineering, ICSE 2024

作者： Wu, Susheng Song, Wenyan Huang, Kaifeng Chen, Bihuan Peng, Xin School of Computer Science Shanghai Key Laboratory of Data Science Fudan University Shanghai China

ISBN: (纸本)9798400702174

Software composition analysis (SCA) tools have been widely adopted to identify vulnerable libraries used in software applications. Such SCA tools depend on a vulnerability database to know affected libraries of each vulnerability. However, it is labor-intensive and error prone for a security team to manually maintain the vulnerability database. While several approaches adopt extreme multi-label learning to predict affected libraries for vulnerabilities, they are practically ineffective due to the limited library labels and the unawareness of ecosystems. To address these problems, we first conduct an empirical study to assess the quality of two fields, i.e., affected libraries and their ecosystems, for four vulnerability databases. Our study reveals notable inconsistency and inaccuracy in these two fields. Then, we propose HOLMES to identify affected libraries and their ecosystems for vulnerabilities via a learning-to-rank technique. The key idea of HOLMES is to gather various evidences about affected libraries and their ecosystems from multiple sources, and learn to rank a pool of libraries based on their relevance to evidences. Our extensive experiments have shown the effectiveness, efficiency and usefulness of HOLMES © 2024 ACM.

关键词： Open source software

来源：评论

学校读者我要写书评

暂无评论

Linking Adaptive Structure Induction and Neuron Filtering: A Spectral Perspective for Aspect-based Sentiment Analysis 30

Linking Adaptive Structure Induction and Neuron Filtering: A...

引用

Joint 30th International Conference on Computational Linguistics and 14th International Conference on Language Resources and Evaluation, LREC-COLING 2024

作者： Niu, Hao Wang, Maoyi Xiong, Yun Yang, Biao Jia, Xing Guo, Zhonglei Shanghai Key Laboratory of Data Science School of Computer Science Fudan University Shanghai China

ISBN: (纸本)9782493814104

Recently, it has been discovered that incorporating structure information (e.g., dependency trees) can improve the performance of aspect-based sentiment analysis (ABSA). The structure information is often obtained from off-the-shelf parsers, which are sub-optimal and unwieldy. Therefore, adaptively inducing task-specific structures is helpful in resolving this issue. In this work, we concentrate on adaptive graph structure induction for ABSA and explore the impact of neuron-level manipulation from a spectral perspective on structure induction. Specifically, we consider word representations from PLMs (pre-trained language models) as node features and employ a graph learning module to adaptively generate adjacency matrices, followed by graph neural networks (GNNs) to capture both node features and structural information. Meanwhile, we propose the Neuron Filtering (NeuLT), a method to conduct neuron-level manipulations on word representations in the frequency domain. We conduct extensive experiments on three public datasets to observe the impact of NeuLT on structure induction and ABSA. The results and further analysis demonstrate that performing neuron-level manipulation through NeuLT can shorten Aspects-sentiment Distance of induced structures and be beneficial to improve the performance of ABSA. The effects of our method can achieve or come close to SOTA (state-of-the-art) performance. © 2024 ELRA Language Resource Association: CC BY-NC 4.0.

关键词： Neurons

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：