检索结果-内蒙古大学图书馆

48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Wang, Dayong Sun, Yu Li, Weisheng Xie, Lele Lu, Xin Dufaux, Frederic Zhu, Ce Chongqing Key Laboratory on Big Data for Bio Intelligence Department of Computer Science China University of Central Arkansas Computing Department of Computer Science United States Chongqing Key Laboratory of Image Cognition School of Information and Communication Engineering China Chongqing University of Posts and Telecommunications School of Information and Communication Engineering China United Kingdom Université Paris-Saclay Cnrs CentraleSupélec Laboratoire des Signaux et Systèmes France University of Electronic Science and Technology of China School of Information and Communication Engineering China

ISBN: (纸本)9781728163277

Due to multi-layer encoding and Inter-layer prediction, Spatial Scalable High-Efficiency Video Coding (SSHVC) has extremely high coding complexity. It is very crucial to improve its coding speed so as to promote widespread and cost-effective SSHVC applications. In this paper, we have proposed a novel Mode Selection-Based Fast Intra Prediction algorithm for SSHVC. We reveal the RD costs of Inter-layer Reference (ILR) mode and Intra mode have a significant difference, and the RD costs of these two modes follow Gaussian distribution. Based on this observation, we propose to apply the classic Gaussian Mixture Model and Expectation Maximization in machine learning to determine whether ILR is the best mode so as to skip the Intra mode. Experimental results demonstrate that the proposed algorithm can significantly improve the coding speed with negligible coding efficiency loss. © 2023 IEEE.

关键词： Gaussian distribution

来源：评论

学校读者我要写书评

暂无评论

Multi-Source Domain Adaptation with Transformer-Based Feature Generation for Subject-Independent EEG-Based Emotion Recognition

Multi-Source Domain Adaptation with Transformer-Based Featur...

引用

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

作者： Shadi Sartipi Mujdat Cetin Department of Electrical and Computer Engineering University of Rochester Rochester NY USA Goergen Institute for Data Science University of Rochester Rochester NY USA

Although deep learning-based algorithms have demonstrated excellent performance in automated emotion recognition via electroencephalogram (EEG) signals, variations across brain signal patterns of individuals can diminish the model’s effectiveness when applied across different subjects. While transfer learning techniques have exhibited promising outcomes, they still encounter challenges related to inadequate feature representations and may overlook the fact that source subjects themselves can possess distinct characteristics. In this work, we propose a multi-source domain adaptation approach with a transformer-based feature generator (MSDA-TF) designed to leverage information from multiple sources. The proposed feature generator retains convolutional layers to capture shallow spatial, temporal, and spectral EEG data representations, while self-attention mechanisms extract global dependencies within these features. During the adaptation process, we group the source subjects based on correlation values and aim to align the moments of the target subject with each source as well as within the sources. MSDA-TF is validated on the SEED dataset and is shown to yield promising results.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Detecting Informationally-Dense Subsets

Detecting Informationally-Dense Subsets

引用

IEEE (first ITW conference of the year) Information Theory Workshop (ITW)

作者： Chao Zhao Ali Al-Bashabsheh Chung Chan Department of Computer Science City University of Hong Kong. Big Data and Brain Computing (BDBC) center at Beihang University Beijing China

ISBN: (数字)9798350348934

ISBN: (纸本)9798350348941

Identifying locally dense subgraphs aims to pinpoint subgraphs characterized by tight internal connectivity. However, existing methods for identifying dense subgraphs based on density can lead to loose internal connections. This paper addresses this issue by introducing a concept of strength to detect strong subsets. Our approach encompasses the existing work that finds a nested chain of densest k-subgraphs as a special case and reveals subgraphs that have tight internal connections overlooked by existing methods. The strong subsets exhibit a laminar structure and can be computed in polynomial time. In contrast to previous works defining locally densest subgraphs without a natural extension to weighted graphs, our method accommodates both weighted and unweighted, directed and undirected graphs, as well as hypergraphs. Furthermore, it extends to a broader notion of information density, surpassing the scope of weighted graphs.

关键词： Conferences Minimization Polynomials Information theory

来源：评论

学校读者我要写书评

暂无评论

Challenges in the Differential Classification of Individual Diagnoses from Co-Occurring Autism and ADHD Using Survey data

Challenges in the Differential Classification of Individual ...

引用

IEEE EMBS International Conference on Information Technology Applications in Biomedicine (ITAB)

作者： Aditi Jaiswal Dennis P. Wall Peter Washington Information and Computer Sciences Department University of Hawaii at Manoa Honolulu USA Departments of Pediatrics & Biomedical Data Science Stanford University Stanford USA

ISBN: (数字)9798350351552

ISBN: (纸本)9798350351569

Autism and Attention-Deficit Hyperactivity Disorder (ADHD) are two of the most commonly observed neurodevelopmental conditions in childhood. Providing a specific computational assessment to distinguish between the two can prove difficult and time intensive. Given the high prevalence of their co-occurrence, there is a need for scalable and accessible methods for distinguishing the co-occurrence of autism and ADHD from individual diagnoses. The first step is to identify a core set of features that can serve as the basis for behavioral feature extraction. We trained machine learning models on data from the National Survey of Children's Health to identify behaviors to target as features in automated clinical decision support systems. A model trained on the binary task of distinguishing either developmental delay (autism or ADHD) vs. neither achieved sensitivity >92% and specificity >94%, while a model trained on the 4-way classification task of autism vs. ADHD vs. both vs. none demonstrated >65% sensitivity and >66% specificity. While the performance of the binary model was respectable, the relatively low performance in the differential classification of autism and ADHD highlights the challenges that persist in achieving specificity within clinical decision support tools for developmental delays. Nevertheless, this study demonstrates the potential of applying behavioral questionnaires not traditionally used for clinical purposes towards supporting digital screening assessments for pediatric developmental delays.

关键词： Surveys Decision support systems Autism Pediatrics Sensitivity Machine learning Feature extraction data models Delays Bioinformatics

来源：评论

学校读者我要写书评

暂无评论

GyroCopter: Differential Bearing Measuring Trajectory Planner for Tracking and Localizing Radio Frequency Sources

arXiv

引用

arXiv 2024年

作者： Chen, Fei Rezatofighi, S. Hamid Ranasinghe, Damith C. The School of Computer & Mathematical Sciences The University of Adelaide SA5005 Australia The Department of Data Science & AI Monash University VIC3800 Australia

Autonomous aerial vehicles can provide efficient and effective solutions for radio frequency (RF) source tracking and localizing problems with applications ranging from wildlife conservation to search and rescue operations. Existing lightweight, low-cost, bearing measurements-based methods with a single antenna-receiver sensor system configurations necessitate in situ rotations, leading to substantial measurement acquisition times restricting searchable areas and number of measurements. We propose a GyroCopter for the task. Our approach plans the trajectory of a multi-rotor unmanned aerial vehicle (UAV) whilst utilizing UAV flight dynamics to execute a constant gyration motion to derive "pseudo-bearing" measurements to track RF sources. The gyration-based pseudo-bearing approach: i) significantly reduces the limitations associated with in situ rotation bearing;while ii) capitalizing on the simplicity, affordability, and lightweight nature of signal strength measurement acquisition hardware to estimate bearings. This method distinguishes itself from other pseudo-bearing approaches by eliminating the need for additional hardware to maintain simplicity, lightweightness and cost-effectiveness. To validate our approach, we derived the optimal rotation speed and conducted extensive simulations and field missions with our GyroCopter to track and localize multiple RF sources. The results confirm the effectiveness of our method, highlighting its potential as a practical and rapid solution for RF source localization tasks. Copyright © 2024, The Authors. All rights reserved.

关键词： Unmanned aerial vehicles (UAV)

来源：评论

学校读者我要写书评

暂无评论

Comparative analysis of deep learning models for detecting face mask 7

Comparative analysis of deep learning models for detecting f...

引用

7th International Conference on computer science and Computational Intelligence, CSCI 2022

作者： Ramadhan, M. Vickya Muchtar, Kahlil Nurdin, Yudha Oktiana, Maulisa Fitria, Maya Maulina, Novi Elwirehardja, Gregorius Natanael Pardamean, Bens Department of Electrical and Computer Engineering Universitas Syiah Kuala Banda Aceh23111 Indonesia Universitas Syiah Kuala Banda Aceh23111 Indonesia Microbiology Department School of Medicine Universitas Syiah Kuala Banda Aceh23111 Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta 11480 Indonesia Computer Science Department BINUS Graduate Program-Master of Computer Science Program Bina Nusantara University Jakarta 11480 Indonesia

The spread of Corona Virus Disease 19 (COVID-19) in Indonesia is still relatively high and has not shown a significant decrease. One of the main reasons is due to the lack of supervision on the implementation of health protocols such as wearing masks in daily activities. Recently, state-of-The-Art algorithms were introduced to automate face mask detection. To be more specific, the researchers developed various kinds of architectures for the detection of masks based on computer vision methods. This paper aims to evaluate well-known architectures, namely the ResNet50, VGG11, InceptionV3, EfficientNetB4, and YOLO (You Only Look Once) to recommend the best approach in this specific field. By using the MaskedFace-Net dataset, the experimental results showed that the EfficientNetB4 architecture has better accuracy at 95.77% compared to the YOLOv4 architecture of 93.40%, InceptionV3 of 87.30%, YOLOv3 of 86.35%, ResNet50 of 84.41%, VGG11 of 84.38%, and YOLOv2 of 78.75%, respectively. It should be noted that particularly for YOLO, the model was trained using a collection of MaskedFace-Net images that had been pre-processed and labelled for the task. The model was initially able to train faster with pre-Trained weights from the COCO dataset thanks to transfer learning, resulting in a robust set of features expected for face mask detection and classification. © 2022 Elsevier B.V.. All rights reserved.

关键词： Face recognition

来源：评论

学校读者我要写书评

暂无评论

Real Time Network Traffic Analysis and Visualization using Wireshark and Google Maps

Real Time Network Traffic Analysis and Visualization using W...

引用

International Conference on Innovative Mechanisms for Industry Applications (ICIMIA)

作者： Swara S Gingade Nagashree B Rishika Mohan V Mohana Computer Science and Engineering (Data Science) RV College of Engineering® Bengaluru India Department of Biotechnology RV College of Engineering® Bengaluru Karnataka India Computer Science and Engineering (Cyber Security) RV College of Engineering® Bengaluru India

This study presents Network Tracker, an innovative solution designed to address the challenge of gaining live insights into network events and better mapping their geographical coverage. In an era where technological advances have raised security and defense systems to new heights, the proliferation of hackers and fraudsters poses a growing threat. Network Tracker uses Wireshark’s robust packet analysis capabilities and integrates with Google Maps-based location visualization. The main objective is to empower users, both businesses and ordinary individuals, to manage their networks in a transparent and proactive manner, highlighting the growing importance of networks, but also increasing the risks associated with them. Further highlights the urgent need for traffic internalization of detailed analytics to gain data stream insights, detect traffic risk and enhance overall network performance increased capabilities Google Maps combined with integration providing detailed, packet-level insights into data streams at a scalable level. Through websites flagged as potentially malicious activity to monitor and trace host connections to these servers, this solution provides users with an effective tool to detect and control address security concerns.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Improving Sign Language Recognition for Deaf and Hard of Hearing Individuals using the VGG16 Model

Improving Sign Language Recognition for Deaf and Hard of Hea...

引用

Automation, Computing and Renewable Systems (ICACRS), International Conference on

作者： Minu Inba Shanthini Watson Benjamin Yaseen Patan Ansilin Kumar S.M Pocharam Nithyanandh Reddy Syed Faheez Ahamad Muvvala Sai Puneeth Department of Computer Science & Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai India Department of Artificial Intelligence and Data Science Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai India

ISBN: (数字)9798331532420

ISBN: (纸本)9798331532437

Persons who are deaf and who cannot speak face a variety of challenges, which emphasises the need for greater research to offer appropriate assistance. Interaction and communication with others are a big challenge. Since sign language is the major means of communication for these people, there is a significant knowledge gap among hearing individuals when it comes to understand and interpret sign language. As a result, there has been a notable expansion in the field of sign language study to close this gap. This work highlights the importance of sign language detection and offers a thorough analysis of relevant studies carried out in this domain using deep learning technique. It approaches the issue from multiple perspective, including translation, sign language recognition, and access to relevant datasets. Understanding these facets is intended to create a meaningful impact on sign language literature and its practical implementation. VGG16 deep learning model for sign language recognition offers the best solution for solving communication challenges faced by deaf and muted individuals. The results show that the VGG16 based technique achieves accuracy of around 95.8%. The VGG16 model uses a Convolutional Neural Network (CNN) architecture, which applies many layers of convolutional filters to identify patterns and attributes in the input images. Recognizing sign language using VGG16

关键词： Deep learning Sign language Visualization Accuracy Translation Face recognition Auditory system Reliability Convolutional neural networks Residual neural networks

来源：评论

学校读者我要写书评

暂无评论

Automated Detection of Alzheimer’s Disease Using EfficientNet-B3 Architecture with Transfer Learning

Automated Detection of Alzheimer’s Disease Using EfficientN...

引用

Information Technology (ACIT)

作者： Ahmad Aburomman Suhaila Abuowaida Adai Al-Momani Esraa Al Henawi Esraa Abu Elsoud Zaher Salah Haneen Alzoubi Huah Yong Chan Mohammed Khouj Department of Data Science and AI Zarqa University Zarqa Jordan Department of Computer Science AI Al-Bayt University Mafraq Jordan Department of Computer Science Zarqa University Zarqa Jordan Department of Information Technology The Hashemite University Zarqa Jordan Faculty of Information Technology Applied Science Private University Amman Jordan School of Computer Sciences Universiti Sains Malaysia Gelugor Pulau Pinang Malaysia Department of Electrical Engineering University of Business and Technology Jeddah Saudi Arabia

ISBN: (数字)9798331540012

ISBN: (纸本)9798331540029

In order to bring forward expansion in both the accuracy and the processing speed of the detection of Alzheimer’s disease, an automated approach for Alzheimer’s disease diagnosis with passable EfficientNet-B3 neural network and transfer learning techniques is provided in this work. After direct fitness on a large number of annotated brain images, the model of EfficientNet-B3 is further trained, in an effort to obtain worse depicted quality connected with Alzheimer’s Disease. To hasten the process of training and to make the model fit for this specific task, transfer learning was employed. The results of these experiments prove the efficiency of our approach to the diagnostic Alzheimer’s disease compared to the most advanced and regular deep learning methods. Thus, the model may also withstand the test amidst diverse clinical populations by performing well in dementia classification with precision and recall performance coupled with over 99% accuracy. These findings demonstrate how the EfficientNet-B3 architecture and transfer learning enhance generalization across.

关键词： Training Deep learning Accuracy Transfer learning Brain modeling Medical diagnosis Alzheimer's disease Information technology Biological neural networks

来源：评论

学校读者我要写书评

暂无评论

Double duality: variational primal-dual policy optimization for constrained reinforcement learning

The Journal of Machine Learning Research

引用

The Journal of Machine Learning Research 2023年第1期24卷 18431-18473页

作者： Zihao Li Boyi Liu Zhuoran Yang Zhaoran Wang Mengdi Wang Department of Electrical and Computer Engineering Princeton University Princeton NJ Department of Industrial Engineering and Management Sciences Northwestern University IL Department of Statistics and Data Science Yale University CT

We study the Constrained Convex Markov Decision Process (MDP), where the goal is to minimize a convex functional of the visitation measure, subject to a convex constraint. Designing algorithms for a constrained convex MDP faces several challenges, including (1) handling the large state space, (2) managing the exploration/exploitation tradeoff, and (3) solving the constrained optimization where the objective and the constraint are both nonlinear functions of the visitation measure. In this work, we present a model-based algorithm, Variational Primal-Dual Policy Optimization (VPDPO), in which Lagrangian and Fenchel duality are implemented to reformulate the original constrained problem into an unconstrained primal-dual optimization. The primal variables are updated by model-based value iteration following the principle of Optimism in the Face of Uncertainty (OFU), while the dual variables are updated by gradient ascent. Moreover, by embedding the visitation measure into a finite-dimensional space, we can handle large state spaces by incorporating function approximation. Two notable examples are (1) Kernelized Nonlinear Regulators and (2) Low-rank MDPs. We prove that with an optimistic planning oracle, our algorithm achieves sublinear regret and constraint violation in both cases and can attain the globally optimal policy of the original constrained problem.

关键词： online learning function approximation reinforcement learning constrained optimization duality theory

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：