检索结果-内蒙古大学图书馆

International Conference on Machine Learning and Applications (ICMLA)

作者： Alaa Nfissi Wassim Bouachir Nizar Bouguila Brian Mishara Data Science Laboratory University of Québec (TÉLUQ) Montréal Canada Concordia Institute for Information Systems Engineering a Concordia University Montréal Canada Centre for Research and Intervention on Suicide Ethical Issues and End-of-Life Practices Montréal Canada Psychology Department University of Québec at Montréal Montréal Canada

ISBN: (数字)9798350374889

ISBN: (纸本)9798350374896

In human-computer interaction and psychological evaluation, speech emotion recognition (SER) is crucial for interpreting emotional states from spoken language. Although there have been advancements, challenges such as system complexity, issues with feature distinctiveness, and noise interference continue to persist. This paper presents a novel end-to-end (E2E) deep learning multi-resolution framework for SER, which tackles these limitations by deriving significant representations directly from raw speech waveform signals. By leveraging the properties of wavelet packet transform (WPT), our approach introduces a learnable model for both wavelet bases and denoising through deep learning techniques. Unlike discrete wavelet transform (DWT), WPT offers a more detailed analysis by decomposing both approximation and detail coefficients, providing a finer resolution in the time-frequency domain. This capability enhances feature extraction by capturing more nuanced signal characteristics across different frequency bands. The framework incorporates a learnable activation function for asymmetric hard thresholding of wavelet packet coefficients. Our approach exploits the capabilities of wavelet packets for effective localization in both time and frequency domains. We then combine one-dimensional dilated convolutional neural networks (1D dilated CNN) with a spatial attention layer and bidirectional gated recurrent units (Bi-GRU) with a temporal attention layer to efficiently capture emotional features' nuanced spatial and temporal characteristics. By handling variable-length speech without segmentation and eliminating the need for pre or post-processing, the proposed model outperforms state-of-the-art methods on our NSPL-CRISE dataset and on the public IEMOCAP dataset. The source code of this paper is shared on this repository: https://***/alaaNfissi/WPT-Deep-Learning-SER-for-Suicide-Monitoring.

关键词： Deep learning Emotion recognition Wavelet domain Accuracy Speech recognition Speech enhancement Wavelet analysis Wavelet packets Discrete wavelet transforms Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

Automatic Life Event Tree Generation for Older Adults 1

引用

24th International Conference on Human-Computer Interaction, HCII 2022

作者： Gui, Fang Wu, Xi Hu, Min Yang, Jiaoyun School of Computer Science and Information Engineering Hefei University of Technology Hefei China Key Laboratory of Knowledge Engineering with Big Data of Ministry of Education Hefei University of Technology Hefei China National Smart Eldercare International S&T Cooperation Base Hefei University of Technology Hefei China Laboratory of Affective Computing and Advanced Intelligent Machine Hefei University of Technology Hefei China Intelligent Interconnected Systems Laboratory of Anhui Province Hefei University of Technology Hefei China

ISBN: (数字)9783031179020

ISBN: (纸本)9783031179013

Studies have shown that learning personal stories could help provide individualized eldercare services. However, personal stories are often disordered because of the scattered collection, including informal interviews or daily interactions, which brings difficulties in acquiring valuable information quickly. One solution to this problem is to extract events from personal stories and automatically organize them in chronological order. Events extracted by current methods from social media or news corpus are mainly organized in a linear structure. These works usually focus on the event time and ignore the consistency of event contents when organizing events. This paper aims to organize events into a tree structure based on an event network, with stem nodes representing key event topics and branch nodes representing detailed events. Social workers or caregivers can clarify the life experience of the older adults quickly through the event tree and have a preliminary understanding of them. The experiments show that the event tree generated by our method has a better performance in consistency than current event organization methods. A survey study shows that our method achieves the highest logical coherence for the event tree branches compared with other algorithms. © 2022, Springer Nature Switzerland AG.

关键词： Trees (mathematics)

来源：评论

学校读者我要写书评

暂无评论

Unlocking the Emotional States of High-Risk Suicide Callers through Speech Analysis

Unlocking the Emotional States of High-Risk Suicide Callers ...

引用

International Conference on Semantic Computing

作者： Alaa Nfissi Wassim Bouachir Nizar Bouguila Brian Mishara Data Science Laboratory University of Québec (TéLUQ) Montréal Canada Concordia Institute for Information Systems Engineering Concordia University Montréal Canada Centre for Research and Intervention on Suicide Ethical Issues and End-of-Life Practices Montréal Canada Psychology Department University of Québec at Montréal Montréal Canada

Suicide remains a major public health concern worldwide, and early detection of suicidal ideation is crucial for prevention. One promising approach for monitoring symptoms is through the prediction of suicidal speech, as speech can be passively collected and may provide insight into changes in risk. However, identifying suicidal speech is challenging due to the rapid variability in speech characteristics and the association of suicidal ideation with emotion dysregulation. In light of these challenges, we present a novel end-to-end (E2E) method for speech emotion recognition (SER) as a mean of detecting changes in emotional state, that may indicate a high risk of suicide. Our method incorporates the use of Convolutional Neural Networks (CNNs) and Gated Recurrent Units (GRUs) to analyze raw waveform signals. Firstly, our model is designed to train the CNN component to identify higher-level speech representations directly from raw waveform data, rather than relying on manually crafted features or spectrograms. This enables the network to effectively capture specific emotion-related features within a narrow frequency range, while also handling speech of varying lengths without the need for segmentation. Secondly, the GRU component is capable of learning temporal patterns, enhancing the network’s ability to capture time-dependent features in signal. Furthermore, we validate our approach on the NSPLCRISE emotion dataset that we recently created. This dataset contains phone call recordings from lifeline frequent callers with psychological problems, and potentially with a history of suicidal ideation and previous attempts. Our experimental results show that our method outperforms other state-of-the-art techniques for SER. The source code of this paper is provided on the Git repository Unlocking the Emotional States of High-Risk Suicide Callers through Speech Analysis.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Contourlet Refinement Gate Framework for Thermal Spectrum Distribution Regularized Infrared Image Super-Resolution

arXiv

引用

arXiv 2024年

作者： Zou, Yang Chen, Zhixin Zhang, Zhipeng Li, Xingyuan Ma, Long Liu, Jinyuan Wang, Peng Zhang, Yanning School of Computer Science Northwestern Polytechnical University Xi’an710129 China Graduate School of Information Production and Systems Waseda University Fukuoka8080135 Japan School of Software Dalian University of Technology Dalian116024 China School of Mechanical Engineering Dalian University of Technology Dalian116024 China The National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology China

Image super-resolution (SR) is a classical yet still active low-level vision problem that aims to reconstruct high-resolution (HR) images from their low-resolution (LR) counterparts, serving as a key technique for image enhancement. Current approaches to address SR tasks, such as transformer-based and diffusion-based methods, are either dedicated to extracting RGB image features or assuming similar degradation patterns, neglecting the inherent modal disparities between infrared and visible images. When directly applied to infrared image SR tasks, these methods inevitably distort the infrared spectral distribution, compromising the machine perception in downstream tasks. In this work, we emphasize the infrared spectral distribution fidelity and propose a Contourlet refinement gate framework to restore infrared modal-specific features while preserving spectral distribution fidelity. Our approach captures high-pass subbands from multi-scale and multi-directional infrared spectral decomposition to recover infrared-degraded information through a gate architecture. The proposed Spectral Fidelity Loss regularizes the spectral frequency distribution during reconstruction, which ensures the preservation of both high- and low-frequency components and maintains the fidelity of infrared-specific features. We propose a two-stage prompt-learning optimization to guide the model in learning infrared HR characteristics from LR degradation. Extensive experiments demonstrate that our approach outperforms existing image SR models in both visual and perceptual tasks while notably enhancing machine perception in downstream tasks. Our code is available at https://***/hey-it-s-me/*** Codes 68T45 Copyright © 2024, The Authors. All rights reserved.

关键词： Infrared imaging

来源：评论

学校读者我要写书评

暂无评论

Exploring Cutting-Edge Machine Learning and data Mining Techniques for Enhancing Big data Management with Advanced Algorithmic Strategies for Optimal data Processing and Analysis

Exploring Cutting-Edge Machine Learning and Data Mining Tech...

引用

International Conference on Advances in information Technology (ICAIT)

作者： M. Anitha V Sheeja Kumari N. Muthuvairavan Pillai P. Jesu Jayarin D. Beulah David Department of Artificial Intelligence & Data Science CARE College of Engineering Trichy Tamil Nadu India Department of Computational Intelligence Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Thandalam Chennai Tamil Nadu India Department of Computer Science and O. Business Systems R.M.D Engineering College Thiruvallur Tamil Nadu India Department of Information Technology Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Thandalam Chennai Tamil Nadu India

ISBN: (数字)9798350383867

ISBN: (纸本)9798350383874

This work focuses on diverse machine learning methods for big data analytics, which works to leverage predictive performance. The techniques like data preprocessing, dimensionality reduction, feature selection, model selection, hyperparameter tuning, and model evaluation are a focus. A number of algorithms including Random Forests, SVM (Support Vector Machines) Gradient Boosting Machines (GBM), Neural Networks, and ensemble methods (Bagging and Boosting) have their performance evaluated. Hyperparameter tuning applies the Grid Search and Random Search algorithms while model evaluation is done by the K-fold Cross-validation. Fleiss' kappa provide differing levels of accuracy, precision, recall, and F1-score for different models and techniques. Ensemble methods actually show how they have all the necessary qualities to win and perform better than individual models. repeatedly testing improve the models' reliability and effectiveness for practical use. The study highlights the crucial significance of choices on the methods and algorithms that suit the peculiarities of the dataset and problem domain. Capitalizing on the machine learning techniques and in-depth evaluation methods, organizations can get the actionable insights from the big data that will ensure the headquarters' decision-making process and accomplish the meaningful outcomes. Future research could instead be exploring new algorithms and techniques to improve the prediction and scalability in big data analytics setting.

关键词： Support vector machines Machine learning algorithms Computational modeling Scalability Software algorithms Big data Prediction algorithms Boosting data models Ensemble learning

来源：评论

学校读者我要写书评

暂无评论

Semi-parametric inference based on adaptively collected data

arXiv

引用

arXiv 2023年

作者： Lin, Licong Khamaru, Koulik Wainwright, Martin J. Department of Electrical Engineering and Computer Sciences United States Department of Statistics UC Berkeley United States Department of Statistics Rutgers University United States Laboratory for Information and Decision Systems United States Statistics and Data Science Center EECS Mathematics Massachusetts Institute of Technology United States

Many standard estimators, when applied to adaptively collected data, fail to be asymptotically normal, thereby complicating the construction of confidence intervals. We address this challenge in a semi-parametric context: estimating the parameter vector of a generalized linear regression model contaminated by a non-parametric nuisance component. We construct suitably weighted estimating equations that account for adaptivity in data collection, and provide conditions under which the associated estimates are asymptotically normal. Our results characterize the degree of "explorability" required for asymptotic normality to hold. For the simpler problem of estimating a linear functional, we provide similar guarantees under much weaker assumptions. We illustrate our general theory with concrete consequences for various problems, including standard linear bandits and sparse generalized bandits, and compare with other methods via simulation studies. Copyright © 2023, The Authors. All rights reserved.

关键词： Linear regression

来源：评论

学校读者我要写书评

暂无评论

Enhancing Physical Layer Communication Security through Generative AI with Mixture of Experts

arXiv

引用

arXiv 2024年

作者： Zhao, Changyuan Du, Hongyang Niyato, Dusit Kang, Jiawen Xiong, Zehui In Kim, Dong Shen, Xuemin Letaief, Khaled B. CNRS@CREATE 1 Create Way 08-01 Create Tower 138602 Singapore College of Computing and Data Science Nanyang Technological University Singapore The School of Automation Guangdong University of Technology China The Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore The Department of Electrical and Computer Engineering Sungkyunkwan University Suwon16419 Korea Republic of The Department of Electrical and Computer Engineering University of Waterloo Canada The Department of Electrical and Computer Engineering Hong Kong University of Science and Technology Hong Kong

AI technologies have become more widely adopted in wireless communications. As an emerging type of AI technologies, the generative artificial intelligence (GAI) gains lots of attention in communication security. Due to its powerful learning ability, GAI models have demonstrated superiority over conventional AI methods. However, GAI still has several limitations, including high computational complexity and limited adaptability. Mixture of Experts (MoE), which uses multiple expert models for prediction through a gate mechanism, proposes possible solutions. Firstly, we review GAI model’s applications in physical layer communication security, discuss limitations, and explore how MoE can help GAI overcome these limitations. Furthermore, we propose an MoE-enabled GAI framework for network optimization problems for communication security. To demonstrate the framework’s effectiveness, we provide a case study in a cooperative friendly jamming scenario. The experimental results show that the MoE-enabled framework effectively assists the GAI algorithm, solves its limitations, and enhances communication security. © 2024, CC BY.

关键词： Network security

来源：评论

学校读者我要写书评

暂无评论

Iterative Feature Boosting for Explainable Speech Emotion Recognition

Iterative Feature Boosting for Explainable Speech Emotion Re...

引用

International Conference on Machine Learning and Applications (ICMLA)

作者： Alaa Nfissi Wassim Bouachir Nizar Bouguila Brian L. Mishara Data Science Laboratory University of Quebec (TÉLUQ) Montreal Canada Concordia Institute for Information Systems Engineering Concordia University Montreal Canada Centre for Research and Intervention on Suicide Ethical Issues and End-of-Life Practices Montreal Canada Psychology Department University of Quebec at Montreal Montreal Canada

In speech emotion recognition (SER), using predefined features without considering their practical importance may lead to high dimensional datasets, including redundant and irrelevant information. Consequently, high-dimensional learning often results in decreasing model accuracy while increasing computational complexity. Our work underlines the importance of carefully considering and analyzing features in order to build efficient SER systems. We present a new supervised SER method based on an efficient feature engineering approach. We pay particular attention to the explainability of results to evaluate feature relevance and refine feature sets. This is performed iteratively through feature evaluation loop, using Shapley values to boost feature selection and improve overall framework performance. Our approach allows thus to balance the benefits between model performance and transparency. The proposed method outperforms human-level performance (HLP) and state-of-the-art machine learning methods in emotion recognition on the TESS dataset. The source code of this paper is publicly available at Iterative-Feature-Boosting-for-Explainable-Speech-Emotion-Recoanition.

关键词：

来源：评论

学校读者我要写书评

暂无评论

A Survey of Social Cybersecurity: Techniques for Attack Detection, Evaluations, Challenges, and Future Prospects

arXiv

引用

arXiv 2025年

作者： Mulahuwaish, Aos Qolomany, Basheer Gyorick, Kevin Abdo, Jacques Bou Aledhari, Mohammed Qadir, Junaid Carley, Kathleen Al-Fuqaha, Ala Department of Computer Science and Information Systems Saginaw Valley State University University Center 48710 United States Department of Medicine College of Medicine Howard University WashingtonDC20059 United States School of Information Technology University of Cincinnati CincinnatiOH45221 United States Department of Data Science University of North Texas DentonTX76207 United States Computer Science and Engineering Department Qatar University Doha Qatar School of Computer Science Carnegie Mellon University 5000 Forbes Ave PittsburghPA15213 United States Hamad Bin Khalifa University Doha Qatar

In today’s age of digital interconnectedness, understanding and addressing the nuances of social cybersecurity have become paramount. Unlike its broader counterparts, information security and cybersecurity, which are focused on safeguarding all forms of sensitive data and digital systems, social cybersecurity places its emphasis on the human and social dimensions of cyber threats. This field is uniquely positioned to address issues such as different social cybersecurity attacks like cyberbullying, cybercrime, spam, terrorist activities, and community detection. The significance of detection methods in social cybersecurity is underscored by the need for timely and proactive responses to these threats. In this comprehensive review, we delve into various techniques, attacks, challenges, potential solutions, and trends within the realm of detecting social cybersecurity attacks. Additionally, we explore the potential of readily available public datasets and tools that could expedite research in this vital domain. Our objective is not only to tackle the existing challenges but also to illuminate potential pathways for future exploration. Through this survey, our primary focus is to provide valuable insights into the rapidly evolving landscape of social cybersecurity. By doing so, we aim to assist researchers and practitioners in developing effective prediction models, enhancing defense strategies, and ultimately fostering a safer digital environment. © 2025, CC BY.

关键词： Cyber attacks

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

science, engineering and Business for Sustainable Development Goals (SEB-SDG), International Conference on

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

ISBN: (数字)9798350358155

ISBN: (纸本)9798350358162

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.

关键词： Computers Cloud computing Organizations Load management Resource management Time factors Task analysis

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：