检索结果-内蒙古大学图书馆

7th International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2025

作者： Oh, Samgwon Oh, Seungmin Lee, Junghoon Kim, Doyeon Hahn, Minsoo Kim, Jinsul Chonnam National University Department of Intelligence Electronics Computer Engineering Gwangju Korea Republic of Chonnam National University Department of Intelligence Information Convergence Gwangju Korea Republic of Astana IT University Department of Computational and Data Science Astana Kazakhstan

ISBN: (纸本)9798331507022

The effective generation of synthetic time-series data is crucial for addressing data scarcity in various domains such as network monitoring, financial analysis, and healthcare. This study explores image-based approaches for transforming time-series data into visual representations, enabling the application of advanced image synthesis techniques. We systematically evaluate multiple transformation methods, including recurrence plots, Gramian Angular Fields (GAF), and Markov transition fields, to identify the most suitable techniques for preserving temporal and structural characteristics. The generated synthetic data are validated using statistical similarity metrics and tested on machine learning tasks, such as anomaly detection and forecasting, to assess their practical utility. Experimental results demonstrate that image-based synthetic data generation not only retains essential time-series features but also enhances model robustness under limited data conditions. This research contributes to the growing field of synthetic data generation by providing insights into the integration of time-series and image processing techniques. Future work will focus on refining these methods and exploring their scalability across diverse applications. © 2025 IEEE.

关键词： Photointerpretation

来源：评论

学校读者我要写书评

暂无评论

Semantic and Emotional Dual Channel for Emotion Recognition in Conversation

引用

IEEE Transactions on Affective Computing 2025年

作者： Yang, Zhenyu Zhang, Zhibo Cheng, Yuhu Zhang, Tong Wang, Xuesong Jinan250353 China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Jinan250353 China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Industrial Network and Information System Security Jinan250353 China China University of Mining and Technology Engineering Research Center of Intelligent Control for Underground Space Ministry of Education Xuzhou Key Laboratory of Artificial Intelligence and Big Data School of Information and Control Engineering Xuzhou221116 China South China University of Technology Guangdong Provincial Key Laboratory of Computational Intelligence and Cyberspace Information School of Computer Science and Engineering Guangzhou510006 China Pazhou Lab Guangzhou510335 China Engineering Research Center Ministry of Education on Health Intelligent Perception and Paralleled Digital-Human Guangzhou China

Emotion recognition in conversation (ERC) aims at accurately identifying emotional states expressed in conversational content. Existing ERC methods, although relying on semantic understanding, often encounter challenges when confronted with incomplete or misleading semantic information. In addition, when dealing with the interaction between emotional and semantic information, existing methods are often difficult to effectively distinguish the complex relationship between the two, which affects the accuracy of emotion recognition. To address the problems of semantic misdirection and emotional cross-talk encountered by traditional models when confronted with complex conversational data, we propose a semantic and emotional dual channel (SEDC) strategy for emotion recognition in conversations to process emotional and semantic information independently. Under this strategy, emotion information provides an auxiliary recognition function when the semantics are unclear or lacking, enhancing the accuracy of the model. Our model consists of two modules: the emotion processing module accurately captures the emotional features of each utterance through contrastive learning, and then constructs a dialogue emotion propagation map to simulate the emotional information conveyed in the dialogue;the semantic processing module combines an external knowledge base to enhance the semantic expression of the dialogue through knowledge enhancement strategies. This divide-and-conquer approach allows us to more deeply analyze the emotional and semantic dimensions of complex dialogues. Experimental results on the IEMOCAP, EmoryNLP, MELD, and DailyDialog datasets show that our approach significantly outperforms existing techniques and effectively improves the accuracy of dialogue emotion recognition. Our code is available at https://***/r/SEDC-FCF1. © 2010-2012 IEEE.

关键词： Contrastive Learning

来源：评论

学校读者我要写书评

暂无评论

Investigation of Automatic Part-of-Speech Tagging using CRF, HMM and LSTM on Misspelled and Edited Texts 5

Investigation of Automatic Part-of-Speech Tagging using CRF,...

引用

5th Artificial Intelligence and Cloud Computing Conference, AICCC 2022

作者： Aydinov, Farhad Huseynov, Igbal Sayadzada, Sofiya Rustamov, Samir Data Science University of Glasgow United Kingdom Computational Linguistics and Language Technologies Charles University Prague Czech Republic Department of Applied Mathematics and Computer Science Technical University of Denmark Copenhagen Denmark School of Information Technology and Engineering ADA University Baku Azerbaijan Institute of Control Systems BakuVarna Bulgaria Azerbaijan

ISBN: (纸本)9781450398749

Part-of-speech tagging is the process of assigning words in a given text to appropriate parts-of speech in order to reduce the disambiguation which may arise depending on the contextual usage of the words. In this paper, the problem of word sense disambiguation in Azerbaijani language is addressed by applying part of speech tagging on two varying data corpora, misspelled, and edited (clean) text using 3 different machine learning algorithms: Hidden Markov Model, Long Short-Term Memory, and Conditional Random Fields. The comparative analysis on the outcomes of the mentioned algorithms and their accuracy scores were analysed in the paper. The misspelled dataset for the experiments is provided by Unibank from their chatbot dialogues while the clean textual data was retrieved from the books and newspapers in Azerbaijani. The experiments showed that the Bidirectional LSTM has the highest accuracy scores for both edited (98.2%) and noisy (96.2%) data corpora. Suggested models can be used in the application of algorithms focuses on part of speech tags and syntactic structure of Azerbaijani language which is an agglutinative language belonging to Turkic languages family, thus enabling the research to be further investigated in other agglutinative languages with similar grammatical structure. © 2022 ACM.

关键词： Hidden Markov models

来源：评论

学校读者我要写书评

暂无评论

Managing Group Decision Making criteria values using Fuzzy Ontologies

引用

Procedia computer science 2022年 199卷 166-173页

作者： J.A. Morente-Molinera F.J. Cabrerizo J.R. Trillo I.J. Pérez E. Herrera-Viedma Department of Computer Science and Artificial Intelligence Andalusian Research Institute in Data Science and Computational Intelligence (DaSCI) University of Granada 18071 Granada Spain Department of Computer Sciences and Engineering University of Cádiz 11519 Puerto Real Spain

Most of the available Multi-criteria Group Decision Making methods that deal with a high number of elements usually focus on managing scenarios that have high number of alternatives and/or experts. Nevertheless, there are also cases in which the number of criteria values is difficult for the experts to tackle. In this paper, a novel Group Decision Making method that employs Fuzzy Ontologies in order to deal with a high number of criteria values is presented. Our method allows the criteria values to be combined in order to generate a reduced set of criteria values that the experts can comfortably deal with.

关键词： Fuzzy Ontologies Multi-criteria Group Decision Making linguistic modelling clustering

来源：评论

学校读者我要写书评

暂无评论

Flow-TSVAD: Target-Speaker Voice Activity Detection via Latent Flow Matching for Speaker Diarization

Flow-TSVAD: Target-Speaker Voice Activity Detection via Late...

引用

International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Zhengyang Chen Bing Han Shuai Wang Yidi Jiang Yanmin Qian Department of Computer Science and Engineering Auditory Cognition and Computational Acoustics Lab MoE Key Lab of Artificial Intelligence AI Institute Shanghai Jiao Tong University Shanghai China Shenzhen Research Institute of Big Data Shenzhen China School of Data Science The Chinese University of Hong Kong Shenzhen China National University of Singapore Singapore

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Speaker diarization is typically considered as a discriminative task, using discriminative approaches to produce fixed diarization results. In this paper, we explore for the first time the use of neural network-based generative methods for speaker diarization. We implement a Flow-Matching (FM) based generative algorithm within the sequenceto-sequence target speaker voice activity detection (Seq2Seq-TSVAD) diarization system. Our experiments reveal that applying the generative method directly to the original binary label sequence space of the TS-VAD output is ineffective. To address this issue, we propose mapping the binary label sequence into a dense latent space before applying the generative algorithm, and our proposed Flow-TSVAD method can significantly outperform the traditional Seq2Seq-TSVAD system. Additionally, we observe that the FM algorithm converges rapidly during the inference stage, only requiring two inference steps to achieve promising results. Moreover, as a generative model, Flow-TSVAD allows for sampling different diarization results by running the model multiple times, so the ensemble system combining the results from various sampling instances can further boost the diarization performance.

关键词： Voice activity detection Frequency modulation computational modeling Signal processing algorithms Signal processing Inference algorithms Acoustics computational efficiency

来源：评论

学校读者我要写书评

暂无评论

Personalized Federated Learning for Smart Healthcare Using SDN-Driven Edge Computing

Personalized Federated Learning for Smart Healthcare Using S...

引用

IEEE Southeastcon

作者： Deborsi Basu Ritu Chaudhary Uttam Ghosh Raja Datta G. S. Sanyal School of Telecommunications Indian Institute of Technology Kharagpur India Dept. of Electronics and Electrical Communication Engineering Indian Institute of Technology Kharagpur India Dept. of Computer Science and Data Science School of Applied Computational Sciences MMC TN USA

ISBN: (数字)9798331504847

ISBN: (纸本)9798331504854

The advancement in distributed Federated Learning (FL) techniques is transforming next-generation edge computing, especially in e-Helathcare systems where data integrity and security are critical concerns. Collecting, processing and analyzing such substantial medical data is highly challenging due to their non-identical and versatile nature. Conventional FL approaches often show unsatisfactory and inaccurate results while operating on those data. Traditional FL methods also lack optimal dynamic management mechanisms for edge devices in ehealthcare, which is crucial to ensure that only authorized devices participate in handling any system failures and new registrations. To tackle such important aspects, in this work, we propose a personalized FL technique to handle abrupt label shifts among clients' data. The proposed approach further targets to achieve personalized models for local edge clients. A statistical batch normalization is proposed on customized edge servers, keeping the data specificity intact. To make the proposed system more secure and robust, a Software Defined Network-based (SDN-based) centralized server is used that allows only authorized and efficient edge clients to participate. The proposed comprehensive experiments on two healthcare datasets demonstrate better accuracy than traditional FL methods with enhanced security and privacy.

关键词： data privacy Federated learning data integrity Medical services Servers Security Software defined networking Next generation networking Batch normalization Edge computing

来源：评论

学校读者我要写书评

暂无评论

Aerodynamic Coefficients Prediction via Cross-Attention Fusion and Physical-Informed Training 39

Aerodynamic Coefficients Prediction via Cross-Attention Fusi...

引用

39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Wang, Yueqing Zhang, Peng Liu, Yushuang Zhao, Jianing Lin, Jie Chen, Yi State Key Laboratory of Aerodynamics Sichuan China Computational Aerodynamics Institute China Aerodynamics Research and Development Center Sichuan China Sichuan Tianfu Fluid Big Data Research Center Chengdu China School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China College of Computer Science and Technology National University of Defense Technology Changsha China

ISBN: (纸本)157735897X

Aerodynamic coefficient prediction is pivotal in aircraft and vehicles’ design, performance evaluation, and motion control. Integrating artificial neural networks into aerodynamic coefficient prediction offers a promising alternative to traditional numerical methods burdened by extensive computations and high costs. Nevertheless, this data-driven approach faces several critical challenges, which limit its further performance enhancement: i) The current research lacks a profound understanding of the complex interplay between the shape of an object and its aerodynamic characteristics. ii) The scarcity of high-quality aerodynamic data poses a significant barrier. The models trained on limited datasets lack generalization ability, struggling to accurately predict and adapt to diverse aerodynamic performance under new shapes or conditions. To overcome these challenges, we introduce an innovative framework that employs cross-attention to capture the intimate interplay between shape and flow conditions and allows for the direct utilization of pre-trained models on general shape datasets to mitigate the scarcity of aerodynamic data. Furthermore, to bolster the inference capabilities of this data-driven approach, we integrate physical information constraints into the model, leveraging them as guiding principles to enhance the model’s predictive power under unknown conditions. Experimental validation demonstrates that our proposed method performs excellently in multiple aerodynamic prediction tasks. This achievement brings a new technological breakthrough to the field of aerodynamic prediction and provides robust support for the design optimization of complex systems such as aircraft and vehicles. © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Technological forecasting

来源：评论

学校读者我要写书评

暂无评论

Intra-datacenter Load Balancing in a Federated Cloud with Throttled Algorithm

Intra-Datacenter Load Balancing in a Federated Cloud with Th...

引用

2024 International Conference on science, engineering and Business for Driving Sustainable Development Goals, SEB4SDG 2024

作者： Akinola, Damola Gideon Adetiba, Emmanuel Abayomi, Abdultaofeek Nnaji, Uche Thakur, Surendra Moyo, Sibusiso Covenant University Covenant Applied Informatics and Communications African Center of Excellence Department of Electrical and Information Engineering Ota Nigeria Durban University of Technology HRA Institute for Systems Science Durban South Africa Mangosuthu University of Technology Department of Information and Communication Technology Durban South Africa Covenant University Dept. of Computer and Information Science Covenant Applied Informatics and Communications African Center of Excellence Ota Nigeria Durban University of Technology KZN E-Skills CoLab Durban South Africa Stellenbosch University School for Data Science and Computational Thinking Department of Mathematical Sciences Stellenbosch7602 South Africa

ISBN: (纸本)9798350358155

The emergence of cloud computing technology has motivated a very high number of users in different organizations to access its services in running and delivering their various operations and services. However, this surge of cloud users has led to the problem of uneven load sharing among the cloud computing infrastructures. As a result, federated cloud infrastructure was initiated to provide more cloud computing resources to accommodate more cloud users' requests and also to provide equal requests mapping with the available cloud resources. Nevertheless, the issue of unequal allocation of requests within the datacenter(s) that makes up the federated cloud environment still persists. This research work presents an intra-datacenter load balancing algorithm in a federated cloud environment named;the intra-balancer adapted throttled algorithm to evenly distribute requests in each datacenter that formulate the federated cloud infrastructures. The simulation of the intra-balancer was carried out in CloudAnalyst, the results of the implementation showed that the intra-balancer outperformed the Round-Robin and ESCE with 98.93ms and 2.26ms for the response and processing time respectively. The results assert that the intra-balancer algorithm provides a better load balancing solution in a federated cloud environment. © 2024 IEEE.

关键词： Cloud platforms

来源：评论

学校读者我要写书评

暂无评论

CFL-Net: Image Forgery Localization Using Contrastive Learning

arXiv

引用

arXiv 2022年

作者： Niloy, Fahim Faisal Bhaumik, Kishor Kumar Woo, Simon S. Center for Computational & Data Sciences Independent University Bangladesh Computer Science and Engineering Department Sungkyunkwan University Suwon Korea Republic of

Conventional forgery localizing methods usually rely on different forgery footprints such as JPEG artifacts, edge inconsistency, camera noise, etc., with cross-entropy loss to locate manipulated regions. However, these methods have the disadvantage of over-fitting and focusing on only a few specific forgery footprints. On the other hand, real-life manipulated images are generated via a wide variety of forgery operations and thus, leave behind a wide variety of forgery footprints. Therefore, we need a more general approach for image forgery localization that can work well on a variety of forgery conditions. A key assumption in underlying forged region localization is that there remains a difference of feature distribution between untampered and manipulated regions in each forged image sample, irrespective of the forgery type. In this paper, we aim to leverage this difference of feature distribution to aid in image forgery localization. Specifically, we use contrastive loss to learn mapping into a feature space where the features between untampered and manipulated regions are well-separated for each image. Also, our method has the advantage of localizing manipulated region without requiring any prior knowledge or assumption about the forgery type. We demonstrate that our work outperforms several existing methods on three benchmark image manipulation datasets. Code is available at https://***/niloy193/CFLNet. © 2022, CC BY.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Distributionally Robust Optimization as a Scalable Framework to Characterize Extreme Value Distributions

arXiv

引用

arXiv 2024年

作者： Kuiper, Patrick Hasan, Ali Yang, Wenhao Ng, Yuting Bidkhori, Hoda Blanchet, Jose Tarokh, Vahid Dept. of Electrical and Computer Engineering Duke University DurhamNC United States Dept. of Management Science and Engineering Stanford University StanfordCA United States Computational and Data Sciences Dept. George Mason University FairfaxVA United States

The goal of this paper is to develop distributionally robust optimization (DRO) estimators, specifically for multidimensional Extreme Value Theory (EVT) statistics. EVT supports using semi-parametric models called max-stable distributions built from spatial Poisson point processes. While powerful, these models are only asymptotically valid for large samples. However, since extreme data is by definition scarce, the potential for model misspecification error is inherent to these applications, thus DRO estimators are natural. In order to mitigate over-conservative estimates while enhancing out-of-sample performance, we study DRO estimators informed by semi-parametric max-stable constraints in the space of point processes. We study both tractable convex formulations for some problems of interest (e.g. CVaR) and more general neural network based estimators. Both approaches are validated using synthetically generated data, recovering prescribed characteristics, and verifying the efficacy of the proposed techniques. Additionally, the proposed method is applied to a real data set of financial returns for comparison to a previous analysis. We established the proposed model as a novel formulation in the multivariate EVT domain, and innovative with respect to performance when compared to relevant alternate proposals. © 2024, CC BY.

关键词： Poisson distribution

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：