检索结果-内蒙古大学图书馆

International Conference on Parallel and Distributed Systems (ICPADS)

作者： Jian Su Zhenlong Liao Qiankun Mao Zhengguo Sheng Alex X. Liu The School of Software Nanjing University of Information Science and Technology Nanjing China The Hwamei College of Life and Health Sciences Zhejiang Wanli University Ningbo China The School of Computer Nanjing University of Information Science and Technology Nanjing China The Department of Engineering and Design University of Sussex Brighton U.K The Department of Computer Science and Engineering Michigan State University East Lansing USA

With the rapid development of the Internet of Things (IoT) related technologies, the application of digital twins (DT) in industry and healthcare becomes possible. Human activity recognition (HAR) is emerging as a hot research area with great potential in healthcare. Activity recognition systems combined with DT will make it easier to monitor human health conditions to improve the quality of life and happiness with individualized healthcare. In this paper, we design an effective HAR system, called HAR-Net, which uses WiFi time series data collected by sensors to train a deep learning network. Deep learning’s great learning ability is utilized to extract features of various human activities for activity recognition. We built the DT system with Unity, which is combined with the HAR system. In the DT system, real-world physical activities are mapped onto human models. The results of activity prediction can be evaluated in real-time in DT, and warnings can be issued quickly when dangerous activities occur. To make our human activity recognition system more adaptive, we propose a one-shot recognition method based on meta-learning. Specifically, we design a Bi-path basic network that extracts features in the time-domain and frequency-domain, and a meta-learning framework with a classification module and a WiFi metric module. Using datasets from different environments, we conducted various experiments on HAR-Net, and the results proved that our presented method was superior to the baseline network.

关键词：

来源：评论

学校读者我要写书评

暂无评论

FGRL-Net: Fine-Grained Personalized Patient Representation Learning for Clinical Risk Prediction Based on EHRs

FGRL-Net: Fine-Grained Personalized Patient Representation L...

引用

IEEE International Conference on Systems, Man and Cybernetics

作者： KaKit Chio Wenhao Zhu Lihua He Dian Zhang Xu Yang Wuman Luo Faculty of Applied Sciences Macao Polytechnic University Macao S.A.R China School of Information and Communication Engineering University of Electronic Science and Technology of China Sichuan Province China Department of Computer Science and Software Engineering Shenzhen University Shenzhen China

Personalized patient representation learning (PPRL) is a critical element in clinical risk prediction. It aims to obtain a complete portrait of each patient based on Electronic Health Records (EHR). Although existing works have achieved remarkable progress in healthcare prediction, there are still three major issues. First, feature correlation is crucial for risk prediction, but it has not yet been fully exploited by existing works. Second, variation pattern of dynamic feature contains useful information about patient's physical status, but adaptive pattern recognition is still a challenge. Third, existing works usually adopt a two-stage embedding process to process each dimension of the EHR data. However, some useful low-level information for PPRL will be lost. To address these issues, in this paper, we propose a fine-grained PPRL architecture named FG RL- N et for clinical risk prediction based on EHR. Specifically, we propose a Medical Feature Correlation Detection Module (FCM) to effectively learn the feature correlations for each patient and a Temporal Variation Pattern Recognition Module (TVM) to effectively detect the variation patterns of each dynamic feature. Moreover, we design a Fine-Grained Representation Mechanism (FGRM) to preserve the low-level information (from both feature and visit dimensions) useful for risk prediction. In addition, in the stage of data preprocessing, We utilize generic medical classification knowledge to classify numerical dynamic data. We conduct the in-hospital mortality experiment and the decompensation experiment on a real-world dataset. The experiment results show that the FGRL-Net outperforms state-of-the-art approaches. The source code is provided in github https://***/JackyChio/FGRL-Net.

关键词：

来源：评论

学校读者我要写书评

暂无评论

SceneGATE: Scene-Graph Based Co-Attention Networks for Text Visual Question Answering

arXiv

引用

arXiv 2022年

作者： Cao, Feiqi Luo, Siwen Nunez, Felipe Wen, Zean Poon, Josiah Han, Soyeon Caren School of Computer Science Faculty of Engineering University of Sydney CamperdownNSW2006 Australia Department of Computer Science and Software Engineering School of Physics Maths and Computing University of Western Australia CrawleyWA6009 Australia

Visual Question Answering (VQA) models fail catastrophically on questions related to the reading of text-carrying images. However, TextVQA aims to answer questions by understanding the scene texts in an image-question context, such as the brand name of a product or the time on a clock from an image. Most TextVQA approaches focus on objects and scene text detection, which are then integrated with the words in a question by a simple transformer encoder. The focus of these approaches is to use shared weights during the training of a multi-modal dataset, but it fails to capture the semantic relations between an image and a question. In this paper, we proposed a Scene Graph-Based Co-Attention Network (SceneGATE) for TextVQA, which reveals the semantic relations among the objects, the Optical Character Recognition (OCR) tokens and the question words. It is achieved by a TextVQA-based scene graph that discovers the underlying semantics of an image. We create a guided-attention module to capture the intra-modal interplay between the language and the vision as a guidance for inter-modal interactions. To permit explicit teaching of the relations between the two modalities, we propose and integrate two attention modules, namely a scene graph-based semantic relation-aware attention and a positional relation-aware attention. We conduct extensive experiments on two widely used benchmark datasets, Text-VQA and ST-VQA. It is shown that our SceneGATE method outperforms existing ones because of the scene graph and its attention modules. Copyright © 2022, The Authors. All rights reserved.

关键词： Neural networks

来源：评论

学校读者我要写书评

暂无评论

Hiding Scrambling Text Message in Speech Signal Based on Lightwight Hyperchaotic Map and Conditional Lsb Mechanisms

SSRN

引用

SSRN 2023年

作者： Al Sibahee, Mustafa A. Abduljaleel, Iman Qays Luo, Chengwen Zhang, Jin Huang, Yijing Abduljabbar, Zaid Ameen Ma, Junchao National Engineering Laboratory for Big Data System Computing Technology Shenzhen Technology University 518060 China Department of Computer Science College of Computer Science and Information Technology University of Basrah 61004 Iraq College of Computer Science and Software Engineering Shenzhen University 518060 China Department of Computer Science College of Education for Pure Sciences University of Basrah Basrah61004 Iraq Technical Computer Engineering Department Al-kunooze University College Basrah61001 Iraq Huazhong University of Science and Technology Shenzhen Institute Shenzhen518118 China College of Big Data and Internet Shenzhen Technology University Shenzhen518118 China

This study proposed a lightweight and secure audio steganography system for hiding text messages during transmission over the Internet to address the computational cost exaggeration, and Insufficient levels of security in earlier studies. The paper proposes a two-phase functioning mechanism. Text characters are transformed into ASCII code and then stored in a vector, which is then divided into three sub-vectors. The sub-vectors are scrambled using a two low complexity operation, namely a forwardbackward reading technique and an odd-even index. Two scrambling circuits can be performed: the first on tiny sub-vectors and another on the vector as a whole. In the hiding phase, the speech signal samples were divided into 256 blocks using only 200 values per block, and low complexity Quadratic and the Hénon maps to hide the speech signal in a random manner. This method utilizes the conditional LSB as low complexity algorithm to identify hidden bits and a special hyperchaotic map algorithm for randomly choosing locations. The proposed approach provides good security for a scrambled text message with high SNR and PSNR, small MSE error and PESQ, SSIM near one, BER close to zero, and MOS near five as well as the lowest computational hiding cost. © 2023, The Authors. All rights reserved.

关键词： Steganography

来源：评论

学校读者我要写书评

暂无评论

Student-Teacher Feature Pyramid Matching for Anomaly Detection 32

Student-Teacher Feature Pyramid Matching for Anomaly Detecti...

引用

32nd British Machine Vision Conference, BMVC 2021

作者： Wang, Guodong Han, Shumin Ding, Errui Huang, Di State Key Laboratory of Software Development Environment Beihang University Beijing China School of Computer Science and Engineering Beihang University Beijing China Department of Computer Vision Technology Baidu Inc. Beijing China

Anomaly detection is a challenging task and usually formulated as an one-class learning problem for the unexpectedness of anomalies. This paper proposes a simple yet powerful approach to this issue, which is implemented in the student-teacher framework for its advantages but substantially extends it in terms of both accuracy and efficiency. Given a strong model pre-trained on image classification as the teacher, we distill the knowledge into a single student network with the identical architecture to learn the distribution of anomaly-free images and this one-step transfer preserves the crucial clues as much as possible. Moreover, we integrate the multi-scale feature matching strategy into the framework, and this hierarchical feature matching enables the student network to receive a mixture of multi-level knowledge from the feature pyramid under better supervision, thus allowing to detect anomalies of various sizes. The difference between feature pyramids generated by the two networks serves as a scoring function indicating the probability of anomaly occurring. Due to such operations, our approach achieves accurate and fast pixel-level anomaly detection. Very competitive results are delivered on the MVTec anomaly detection dataset, superior to the state of the art ones. © 2021. The copyright of this document resides with its authors.

关键词： Anomaly detection

来源：评论

学校读者我要写书评

暂无评论

Multiple attributes k-means clustering for elastic cloud model

Multiple attributes k-means clustering for elastic cloud mod...

引用

2020 International Symposium on Networks, computers and Communications, ISNCC 2020

作者： Daradkeh, Tariq Agarwal, Anjali Alahmad, Yanal Concordia University Department of Electrical and Computer Engineering Montreal Canada Concordia University Department of Computer Science and Software Engineering Montreal Canada

ISBN: (纸本)9781728156286

Elastic cloud computing model rely on clear definition of workload demand capacity size and cloud resources provision units. These two factors are unknown for any running cloud model, because of the dynamic changes of workload and cloud data center provisioning resources reconfiguration characteristics. These can be defined as unlabeled data. To achieve an accurate elastic scaling, unlabeled data set should be marked and labeled to finite set of workload demand classes and provisioned resources classes. This work introduces a multiple criteria attribute, k-means clustering, for cloud data center elastic model to achieve a commensurate mapping between workload class and provisioned class. Two validation methods for k-means clustering have been applied to validate the cluster group sets, obtaining a good and reasonable mapping for demand and provisioned classes with accepted time and space complexity. Two groups of sets have been generated for workload demands and for resources provisioned, and a simple look-up mapping has been applied using set joint theory. © 2020 IEEE.

关键词： Mapping

来源：评论

学校读者我要写书评

暂无评论

Rank Aggregation Based Multi-filter Feature Selection Method for software Defect Prediction 2nd

Rank Aggregation Based Multi-filter Feature Selection Method...

引用

2nd International Conference on Advances in Cyber Security, ACeS 2020

作者： Balogun, Abdullateef O. Basri, Shuib Abdulkadir, Said Jadid Mahamad, Saipunidzam Al-momamni, Malek A. Imam, Abdullahi A. Kumar, Ganesh M. Department of Computer and Information Sciences Universiti Teknologi PETRONAS Bandar Seri Iskandar Perak32610 Malaysia Department of Computer Science University of Ilorin Ilorin1515 Nigeria Department of Software Engineering The World Islamic Sciences and Education University Amman11947 Jordan

ISBN: (纸本)9789813368347

With the variety of different filter methods, selecting the most appropriate filter method which gives the best performance is a difficult task. Filter rank selection and stability problems make the selection of filter methods in SDP a hard choice. The best approach is to independently apply a mixture of filter methods and evaluate the results. This study presents a novel rank aggregation-based multi-filter feature selection method to address high dimensionality and filter rank selection problems in software defect prediction. The proposed method combines the rank list generated by individual filter methods from the software defect dataset using a rank aggregation mechanism into a single aggregated rank list. The proposed method aims to resolve the filter selection problem by using multiple filter methods of diverse computational characteristics to produce a more stable (non-disjoint) and complete feature rank list better than individual filter methods employed. The effectiveness of the proposed method was evaluated by applying with Decision Tree (DT) and Naïve Bayes (NB) models on defect datasets from NASA repository. From the experimental results, the proposed method had a superior effect (positive) on the prediction performance of selected prediction models than other experimented methods. This makes the combining of individual filter rank methods a viable solution to the filter rank selection problem and enhancement of prediction models in SDP. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Decision trees

来源：评论

学校读者我要写书评

暂无评论

Transformers Fusion across Disjoint Samples for Hyperspectral Image Classification

arXiv

引用

arXiv 2024年

作者： Ahmad, Muhammad Mazzara, Manuel Distifano, Salvatore The Department of Computer Science National University of Computer and Emerging Sciences Islamabad Chiniot-Faisalabad Campus Chiniot35400 Pakistan Dipartimento di Matematica e Informatica-MIFT University of Messina Messina98121 Italy The Institute of Software Development and Engineering Innopolis University Innopolis420500 Russia

3D Swin Transformer (3D-ST) known for its hierarchical attention and window-based processing, excels in capturing intricate spatial relationships within images. Spatial-spectral Transformer (SST), meanwhile, specializes in modeling long-range dependencies through self-attention mechanisms. Therefore, this paper introduces a novel method: an attentional fusion of these two transformers to significantly enhance the classification performance of Hyperspectral Images (HSIs). What sets this approach apart is its emphasis on the integration of attentional mechanisms from both architectures. This integration not only refines the modeling of spatial and spectral information but also contributes to achieving more precise and accurate classification results. The experimentation and evaluation of benchmark HSI datasets underscore the importance of employing disjoint training, validation, and test samples. The results demonstrate the effectiveness of the fusion approach, showcasing its superiority over traditional methods and individual transformers. Incorporating disjoint samples enhances the robustness and reliability of the proposed methodology, emphasizing its potential for advancing hyperspectral image classification. © 2024, CC BY.

关键词： Image enhancement

来源：评论

学校读者我要写书评

暂无评论

A Dynamic SDN-based Privacy-Preserving Approach for Smart City Using Trust Technique 9

A Dynamic SDN-based Privacy-Preserving Approach for Smart Ci...

引用

9th Iranian Joint Congress on Fuzzy and Intelligent Systems, CFIS 2022

作者： Alzubi, Jafar A. Movassagh, AliAkbar Gheisari, Mehdi Najafabadi, Hamid Esmaeili Abbasi, Aaqif Afzaal Liu, Yang Pingmei, Zhou Izadpanahkakhk, Mahdieh Najafabadi, AmirHossein Pourishaban Al-Balqa Applied University Faculty of Engineering Salt Jordan Tehran University of Medical Sciences School of Medicine Department of Medical Physics and Biomedical Engineering Tehran Iran Islamic Azad University Young Researchers and Elite Club Parand Branch Parand Iran University of Calgary Canada Foundation University Department of Software Engineering Islamabad44000 Pakistan Harbin Institute of Technology Department of Computer Science and Technology Shenzhen China Shenzhen BKD Company Shenzhen China La Sapienza University DIET Department Rome Italy University of Yazd Department of Electrical and Computer Engineering Yazd Iran

ISBN: (纸本)9781665478724

A smart city is an Internet-based application of things that automates city management with no need for human interference. Exchanging data via devices obviate some challenges in intelligent cities. In a smart city, Internet-of-Things (IoT) devices may detect sensitive data, posing a risk of privacy violation and system harm. We discover that existing solutions are either too expensive or ineffective at limiting unintended disclosure of sensitive data to build a dependable, smart city. The fact that they create static surroundings is the fundamental reason behind this. software-Defined Networking (SDN) technology has recently evolved to configure the network for performance and monitoring improvement. This study offers a work-in-progress that uses the SDN to protect the privacy of IoT devices by creating a dynamic SDN-based privacy-preserving ecology. The mechanism of the SDN controller performs under the nodes' mutual trust;it chooses various routes from the IoT device to the Cloud space destination dependent on the level of confidence. The packet is re-routed if the SDN controller identifies a device that does not trust its neighbor. Then it instructs the owner to deliver data over a different path. To demonstrate its improved performance, we are currently evaluating it from the perspective of overhead criteria in the future. © 2022 IEEE.

关键词： Smart city

来源：评论

学校读者我要写书评

暂无评论

Lprr: Locality Preserving Robust Regression Based Sparse Feature Extraction

SSRN

引用

SSRN 2024年

作者： Zhu, Yufei Wen, Jiajun Lai, Zhihui Zhou, Jie Kong, Heng The Computer Vision Institute The College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China The Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen University Shenzhen518060 China the Department of Breast and Thyroid Surgery BaoAn Central Hospital of Shenzhen Shenzhen518060 China

Jointly sparse projection learning attracts considerable attention due to its strong interpretability in feature extraction. To address the challenges related to weak discriminating representation in supervised feature extraction, we propose a more powerful regression framework. Based on the framework, we exhibit a new regression model called locality preserving robust regression (LPRR). In LPRR, we first combine the reconstruction error minimization and the projection variance maximization to explore the structured information of the data. Then, the label information is utilized and the low rank representation can be learned to explore the latent correlation structures among different classes. Furthermore, L2,1-norm is applied to measure the loss function and regularization terms, enhancing the robustness of the model and ensuring the joint sparsity of the projection matrix. An iterative algorithm is elaborately designed to achieve the optimal solutions of LPRR, in which the subproblem of LPRR can be regarded as a general quadratic problem on the Stiefel manifold. The convergence and the computational complexity of LPRR are analyzed rigorously. Finally, comprehensive experiments demonstrate the competitive performance of the proposed algorithm. © 2024, The Authors. All rights reserved.

关键词： Feature extraction

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：