检索结果-内蒙古大学图书馆

5th IEEE International Conference on Pattern Recognition and Machine Learning, PRML 2024

作者： Si, Ha La, Du Ding, Yang College of Computer Science and Information Engineering Hohhot Minzu College Hohhot China College of Computer Science and Technology Inner Mongolia Normal University Hohhot China School of Software and Big Data Inner Mongolia Electronic Information Vocational Technical College Hohhot China

ISBN: (纸本)9798350355925

Mongolian compound nouns often become the vehicle for the creation of new concepts and terms due to their diverse combinations and flexible expressive power. In the field of natural language shallow syntactic analysis, the recognition of Mongolian compound nouns plays an important role, and its performance will directly affect the processing effect of related research. In the current academic research, the research on automatic recognition of Mongolian compound nouns is still insufficient, especially in the automatic recognition of Mongolian compound nouns using deep learning technology, which is almost in a blank state. In this study, we firstly construct a Mongolian compound noun annotated corpus, and on the basis of the constructed annotated corpus, we propose a Mongolian compound noun automatic recognition model based on deep learning for the fact that there is no deep learning method for recognising Mongolian compound nouns at present. The model takes Conditional Random Field (CRF) as the basic framework to make full use of contextual information by considering the joint probability distribution of the whole sequence. During the model construction process, the TextCNN model is incorporated and by designing a multi-scale convolutional kernel, the model is able to effectively capture the local features of Mongolian compound nouns. The experimental results show that the automatic Mongolian compound noun recognition model based on deep learning proposed in this study performs well on the Mongolian compound noun recognition task, with an F1 value of 93.40%. © 2024 IEEE.

关键词： Contrastive Learning

来源：评论

学校读者我要写书评

暂无评论

Stroke Disease Prediction Based on Multi-Model Ensemble Learning

Stroke Disease Prediction Based on Multi-Model Ensemble Lear...

引用

Artificial Intelligence, Networking and Information Technology (AINIT), International Seminar on

作者： Xiaojian Cui School of Computer Science and Engineering / School of Software Guangxi Normal University Guilin China

ISBN: (数字)9798350385557

ISBN: (纸本)9798350385564

Stroke is a kind of acute cerebrovascular disease, which is caused by the sudden rupture of blood vessels in the brain or the blockage of blood vessels that can not flow into the brain and cause brain tissue damage. Stroke has the characteristics of acute onset and rapid progress. Once stroke, it can lead to extremely serious consequences, and many people suffer from it. But because of the lack of effective treatment for stroke, prevention is the best response. So we use multi-model ensemble learning method to train a model. It can analyze biological signals and lifestyle habits to predict the probability of a person having a stroke. People can adjust their living habits in time according to the probability of stroke. In order to reduce the probability of stroke, protect their own life and health. According to our experiments, the accuracy of the trained model is high, which can help people to prevent stroke disease.

关键词： Seminars Brain Accuracy Tissue damage Prevention and mitigation Predictive models Blood vessels

来源：评论

学校读者我要写书评

暂无评论

Why Zero Trust Framework Adoption has Emerged During and After Covid-19 Pandemic 1

引用

37th International Conference on Advanced Information Networking and Applications, AINA 2023

作者： Alalmaie, Abeer Z. Nanda, Priyadarsi He, Xiangjian Alayan, Mohrah Saad School of Electrical and Data Engineering University of Technology Sydney Sydney Australia School of Computer Science University of Nottingham Ningbo Ningbo China School of Software University of Technology Sydney Sydney Australia

ISBN: (数字)9783031286940

ISBN: (纸本)9783031286933

The rise of COVID-19 brought an unprecedented change in the way people lived. It left several people in a work-from-home situation. This Paper aims to investigate the recent works which applied Zero Trust and the reason that this framework adoption has emerged during and after the Pandemic. In this regard, a questionnaire was prepared, and its results are reported. According to its results, with Zero Trust Architecture (ZTA) gaining skyrocket popularity and trust, for around 60% corporates, ZT Access is planned for future, while for around 30% corporates, the project is in pipeline. None of the organizations surveyed have the ZTA in place. 14% of organizations are uninterested in adopting ZTA. Plus, in past 2 years, the percentage of north American organizations having a ZTA on the plans to establish one in the next 12–18 months has shot up. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： COVID-19

来源：评论

学校读者我要写书评

暂无评论

ECNU_MIV at SemEval-2023 Task 1: CTIM - Contrastive Text-Image Model for Multilingual Visual Word Sense Disambiguation 17

ECNU_MIV at SemEval-2023 Task 1: CTIM - Contrastive Text-Ima...

引用

17th International Workshop on Semantic Evaluation, SemEval 2023, co-located with the 61st Annual Meeting of the Association for Computational Linguistics, ACL 2023

作者： Li, Zhenghui Zhang, Qi Xia, Xueyin Ye, YinXiang Huang, Cong School of Computer Science and Technology East China Normal University China School of Communication and Electronic Engineering East China Normal University China School Of Software JiangXi Agricultural University China

ISBN: (纸本)9781959429999

This paper describes the performance of MIV team in SemEval-2023 Task-1-Visual Word Sense Disambiguation (Visual-WSD). This task is to give a potentially ambiguous word and some limited textual context and select among a set of candidate images the one which corresponds to the intended meaning of the target word, which plays a critical role in human language understanding. Our team focuses on the multimodal domain of images and texts, we propose a model that can learn the matching relationship between text-image pairs by contrastive learning. More specifically, We train the model from the labeled data provided by the official organizer, after pre-training, texts are used to reference learned visual concepts enabling visual word sense disambiguation tasks. In addition, the top results our teams got have been released showing the effectiveness of our solution. We release our code at https://***/Insaner1004/VWSD. © 2023 Association for Computational Linguistics.

关键词： Semantics

来源：评论

学校读者我要写书评

暂无评论

A missing physical fitness test data classification method based on MLP 10

A missing physical fitness test data classification method b...

引用

10th IEEE International Conference on Cyber Security and Cloud Computing and 9th IEEE International Conference on Edge Computing and Scalable Cloud, CSCloud-EdgeCom 2023

作者： Che, Peng Peng, Zhenlian Cao, Buqing Liu, Jianxun Chen, Tieping Fan, Runqing Hunan University of Science and Technology School of Computer Science and Engineering Hunan Provincial Key Laboratory for Services Computing and Novel Software Technology Xiangtan China Hunan University of Science and Technology School of Physical Education Xiangtan China

ISBN: (纸本)9798350312461

In recent years, the physical health status of college students have been attached by the country and all universities. Much effort is spent every year to conduct physical health tests, generating a large amount of physical fitness data. Due to some irresistible reasons, such as entering incorrect data or students being unable to participate in some physical fitness programs, missing data exists in the physical fitness test, and the calculation of total points according to the existing rules of weighted summation may lead to inaccurate classification of some students grades. To this end, a classification method based on physical fitness test data with missing items is proposed in this paper. Firstly, the method collects the raw data set and constructs a structured data set. Then, the MLP method is used to construct a classifier model to classify the test data set with missing items. Finally, compared experiments are conducted with the training model constructed by GaussianNB, SVM and XGBoost and the results show that the classification effect of MLP is excellent, and the accuracy of classification can reach 93.29 %. © 2023 IEEE.

关键词： Classification (of information)

来源：评论

学校读者我要写书评

暂无评论

A Framework for Facilitating Reproducible News Sentiment Impact Analysis 2022

A Framework for Facilitating Reproducible News Sentiment Imp...

引用

5th International Conference on software engineering and Information Management, ICSIM 2022

作者： Chen, Weisi Al-Qudah, Islam Rabhi, Fethi School of Software Engineering Xiamen University of Technology China Department of Computer Science University of Sharjah United Arab Emirates School of Computer Science and Engineering University of New South Wales Australia

ISBN: (纸本)9781450395519

The proliferation of outlets for news media in recent decades has contributed to faster issuance of news data. News analysis has been one of the key activities conducted by researchers in a broad variety of research disciplines. In general, the analysis process used in these studies includes interpreting the content of the news items, and then discovering their impact in a specific area. In this paper, we delve into the field of news analysis applied to the financial domain and explore news sentiment impact analysis in the context of financial markets. Existing studies lack systematic methods to assimilate financial context and evaluate the impact of a given news dataset on relevant entities financial market performance. We introduce an improved version of the framework called News Sentiment Impact Analysis (NSIA) that encompasses models, supporting software architecture and processes for defining various financial contexts and conducting news sentiment impact analysis. The framework is then evaluated using a prototype implementation and a case study that investigates the impact of extremely negative news on the stock price of the related entities. The results demonstrate the functionality, usability and reproducibility of the framework, and its capability to bridge the gap between generating news sentiment and evaluating its impact in selected financial contexts. © 2022 ACM.

关键词： software architecture

来源：评论

学校读者我要写书评

暂无评论

XRL-SHAP-Cache:an explainable reinforcement learning approach for intelligent edge service caching in content delivery networks

引用

science China(Information sciences) 2024年第7期67卷 46-71页

作者： Xiaolong XU Fan WU Muhammad BILAL Xiaoyu XIA Wanchun DOU Lina YAO Weiyi ZHONG School of Software Nanjing University of Information Science and Technology School of Computing and Communications Lancaster University School of Computing Technologies Royal Melbourne Institute of Technology State Key Laboratory for Novel Software Technology Nanjing University School of Computer Science and Engineering University of New South Wales Data 61 Commonwealth Scientific and Industrial Research Organization School of Computer Science Qufu Normal University

Content delivery networks(CDNs) play a pivotal role in the modern internet infrastructure by enabling efficient content delivery across diverse geographical regions. As an essential component of CDNs, the edge caching scheme directly influences the user experience by determining the caching and eviction of content on edge servers. With the emergence of 5G technology, traditional caching schemes have faced challenges in adapting to increasingly complex and dynamic network environments. Consequently, deep reinforcement learning(DRL) offers a promising solution for intelligent zero-touch network governance. However, the blackbox nature of DRL models poses challenges in understanding and making trusting decisions. In this paper,we propose an explainable reinforcement learning(XRL)-based intelligent edge service caching approach,namely XRL-SHAP-Cache, which combines DRL with an explainable artificial intelligence(XAI) technique for cache management in CDNs. Instead of focusing solely on achieving performance gains, this study introduces a novel paradigm for providing interpretable caching strategies, thereby establishing a foundation for future transparent and trustworthy edge caching solutions. Specifically, a multi-level cache scheduling framework for CDNs was formulated theoretically, with the D3QN-based caching scheme serving as the targeted interpretable model. Subsequently, by integrating Deep-SHAP into our framework, the contribution of each state input feature to the agent's Q-value output was calculated, thereby providing valuable insights into the decision-making process. The proposed XRL-SHAP-Cache approach was evaluated through extensive experiments to demonstrate the behavior of the scheduling agent in the face of different environmental *** results demonstrate its strong explainability under various real-life scenarios while maintaining superior performance compared to traditional caching schemes in terms of cache hit ratio, quality of service(QoS),a

关键词： deep reinforcement learning (DRL) explainable artificial intelligence (XAI) multi-level cache content delivery network (CDN) D3QN algorithm Deep-SHAP

来源：评论

学校读者我要写书评

暂无评论

Online Optimization of DNN Inference Network Utility in Collaborative Edge Computing

arXiv

引用

arXiv 2024年

作者： Li, Rui Ouyang, Tao Zeng, Liekang Liao, Guocheng Zhou, Zhi Chen, Xu School of Computer Science and Engineering China School of Software Engineering Sun Yat-Sen University China

Collaborative Edge Computing (CEC) is an emerging paradigm that collaborates heterogeneous edge devices as a resource pool to compute DNN inference tasks in proximity such as edge video analytics. Nevertheless, as the key knob to improve network utility in CEC, existing works mainly focus on the workload routing strategies among edge devices with the aim of minimizing the routing cost, remaining an open question for joint workload allocation and routing optimization problem from a system perspective. To this end, this paper presents a holistic, learned optimization for CEC towards maximizing the total network utility in an online manner, even though the utility functions of task input rates are unknown a priori. In particular, we characterize the CEC system in a flow model and formulate an online learning problem in a form of cross-layer optimization. We propose a nested-loop algorithm to solve workload allocation and distributed routing iteratively, using the tools of gradient sampling and online mirror descent. To improve the convergence rate over the nested-loop version, we further devise a single-loop algorithm. Rigorous analysis is provided to show its inherent convexity, efficient convergence, as well as algorithmic optimality. Finally, extensive numerical simulations demonstrate the superior performance of our solutions. Copyright © 2024, The Authors. All rights reserved.

关键词： Mirrors

来源：评论

学校读者我要写书评

暂无评论

ArcText: A Unified Text Approach to Describing Convolutional Neural Network Architectures

IEEE Transactions on Artificial Intelligence

引用

IEEE Transactions on Artificial Intelligence 2022年第4期3卷 526-540页

作者： Sun, Yanan Yen, Gary G. Xue, Bing Zhang, Mengjie Lv, Jiancheng Sichuan University College of Computer Science Chengdu610065 China Nanjing University State Key Laboratory for Novel Software Technology Nanjing210023 China Oklahoma State University School of Electrical and Computer Engineering StillwaterOK74078 United States Victoria University of Wellington School of Engineering and Computer Science Wellington6140 New Zealand

The superiority of convolutional neural networks (CNNs) largely relies on their architectures that are usually manually crafted with extensive human expertise. Unfortunately, such kind of domain knowledge is not necessarily owned by every interested user. Data mining on existing CNNs can discover useful patterns and fundamental comments from their architectures, providing researchers with strong prior knowledge to design effective CNN architectures when they have no expertise in CNNs. There are various state-of-the-art data mining algorithms at hand, while there is only rare work on mining CNN architectures. One of the main reasons is the gap between CNN architectures and data mining algorithms. Specifically, the current CNN architecture descriptions cannot be exactly vectorized to feed to a data mining algorithm. In this article, we propose a unified approach, named ArcText, to describing CNN architectures based on text. Particularly, four different units and an ordering method have been elaborately designed in ArcText, to uniquely describe the same CNN architecture with sufficient information. Also, the resulted description can be exactly converted back to the corresponding CNN architecture. ArcText bridges the gap between CNN architectures and data mining researchers, and has the potential to be utilized to wider scenarios. © 2020 IEEE.

关键词： Data mining

来源：评论

学校读者我要写书评

暂无评论

Multi-View Incongruity Learning for Multimodal Sarcasm Detection 31

Multi-View Incongruity Learning for Multimodal Sarcasm Detec...

引用

31st International Conference on Computational Linguistics, COLING 2025

作者： Guo, Diandian Cao, Cong Yuan, Fangfang Liu, Yanbing Zeng, Guangjie Yu, Xiaoyan Peng, Hao Yu, Philip S. Institute of Information Engineering Chinese Academy of Sciences China School of Cyber Security University of Chinese Academy of Sciences China State Key Laboratory of Software Development Environment Beihang University China School of Computer Science and Technology Beijing Institute of Technology China Department of Computer Science University of Illinois Chicago United States

ISBN: (纸本)9798891761964

Multimodal sarcasm detection (MSD) is essential for various downstream tasks. Existing MSD methods tend to rely on spurious correlations. These methods often mistakenly prioritize non-essential features yet still make correct predictions, demonstrating poor generalizability beyond training environments. Regarding this phenomenon, this paper undertakes several initiatives. Firstly, we identify two primary causes that lead to the reliance of spurious correlations. Secondly, we address these challenges by proposing a novel method that integrate Multimodal Incongruities via Contrastive Learning (MICL) for multimodal sarcasm detection. Specifically, we first leverage incongruity to drive multi-view learning from three views: token-patch, entity-object, and sentiment. Then, we introduce extensive data augmentation to mitigate the biased learning of the textual modality. Additionally, we construct a test set, SPMSD, which consists potential spurious correlations to evaluate the the model's generalizability. Experimental results demonstrate the superiority of MICL on benchmark datasets, along with the analyses showcasing MICL's advancement in mitigating the effect of spurious correlation. © 2025 Association for Computational Linguistics.

关键词： Contrastive Learning

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：