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Eulerian Algorithms for computing the Forward Finite Time Lyapunov Exponent Without Finite Difference Upon the Flow Map

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Communications in Computational Physics 2022年第5期31卷 1467-1488页

作者： Guoqiao You Changfeng Xue Shaozhong Deng School of Statistics and Data Science Nanjing Audit UniversityNanjingJiangsu 211815P.R.China School of Mathematics and Physics Yancheng Institute of TechnologyYanchengJiangsu 224051P.R.China Department of Mathematics and Statistics University of North Carolina at CharlotteCharlotteNC 28223-0001USA

In this paper,effective Eulerian algorithms are introduced for the computation of the forwardfinite time Lyapunov exponent(FTLE)of *** advantages of the proposed algorithms mainly manifest in two ***,previous Eulerian approaches for computing the FTLEfield are improved so that the Jacobian of theflow map can be obtained by directly solving a corresponding system of partial differential equations,rather than by implementing certainfinite difference upon theflow map,which can significantly improve the accuracy of the numerical solution especially near the FTLE ***,in the proposed algorithms,all computations are done on thefly,that is,all required partial differential equations are solved forward in time,which is practically more *** new algorithms still maintain the optimal computational complexity as well as the second order *** examples demonstrate the effectiveness of the proposed algorithms.

关键词： Finite time Lyapunov exponent flow map flow visualization dynamical system

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Cost-Effective Communication in UDN in Indoor and Outdoor Environment via Machine Learning

Cost-Effective Communication in UDN in Indoor and Outdoor En...

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2023 International Conference on Artificial Intelligence and Knowledge Discovery in Concurrent Engineering, ICECONF 2023

作者： Karman, K. Nattar Velmurugan, V. Raju, Kommisetti Murthy Sajana, T. Vijayalakshmi, V. Dhanraj, JoshuvaArockia Department of Artificial Intelligence and Machine Learning Saveetha School of Engineering Tamil Nadu Chennai602105 India Department of Electronics and Communication Engineering Vel Tech Rangarajan and Dr.Sagunthala RandD Institute of Science and Technology Tamil Nadu Chennai600062 India Department of Electronics and Communication Engineering Shri Vishnu Engineering College for Women Andhra Pradesh West Godavari India Department of Artificial Intelligence and Data Science KoneruLakshmaiah Education Foundation Andhra Pradesh Vaddeswaram522502 India Department of Networking and Communications School of Computing SRM Institute of Science and Technology Tamil Nadu Kattankulathur603203 India Department of Mechatronics Engineering Hindustan Institute of Technology and Science Tamil Nadu Chennai603103 India

ISBN: (纸本)9798350334357

In general, applications on a densely populated network are slower. When there are no opportunities to interact with the devices on the network, the user is forced to communicate at some cost. Thus, the inconsistency that exists there increases the cost over that dense network. The single way to evaluate prospect usage is to look at that have used in the earlier period. This paper proposes an upgraded approach to upgrading existing services within the Ultra Dense cloud Network (UDN). In this way the cost for the services available there is calculated in advance and a system is developed to manage it effectively. This proposed method clearly shows that the effective cost can be managed by combining high-costservices and low-costservices separately. This will help to manage the various communication processes to achieve high effectiveness in Ultra Dense cloud Network (UDN). The proposed method achieves 93.28% of Cost-effective data Management.49% of Application log failure, 42 % of system log failure, 98% of network log management, 96% of security log management, 99% cost effective management. © 2023 IEEE.

关键词： Cost effectiveness

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Consensus Graph Filter Learning for Multiple Graph Clustering

Consensus Graph Filter Learning for Multiple Graph Clusterin...

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

作者： Jiale Zou Yan Chen Peng Zhou Chao Wen Liang Du Yuhua Qian School of Computer and Information Technology Shanxi University Taiyuan China College of Computer Science and Technology Taiyuan University of Technology Taiyuan China School of Computer Science and Technology Anhui University Hefei China Institute of Big Data Science and Industry Shanxi University Taiyuan China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Multi-view Clustering (MVC) has gained significant attention for its ability to utilize consistent and complementary information from multiple views. Graph filter-based MVC methods have recently demonstrated promising performance, attracting growing interest. However, existing graph filter-based methods typically rely on a single filter for each view. These filters are usually derived from either a specific view or a consensus graph across all views, which limits their effectiveness in fully integrating multi-view information. To address this limitation, we propose a novel method, Consensus Graph Filter Learning for Multiple Graph Clustering (CGFMVC). Unlike existing methods that rely on a single filter per view, we construct multiple graph filters by integrating information from all views. For each view, we generate intra-view graph filters to intermediate high-order information and bridge local-global structures. This approach facilitates neighborhood smoothing and preserves local consistency within each view. By learning a consensus graph filter from these multiple filters, we effectively capture global complementary information across views while preserving local consistency. CGFMVC demonstrates excellent efficiency and effectiveness on various benchmark datasets, outperforming state-of-the-art methods. The code is publicly available at https://***/Sean-zjl/CGFMVC.

关键词： Bridges Smoothing methods Codes Benchmark testing Signal processing Information filters Acoustics Speech processing

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Comparison of bulk snow density measurements using different methods

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Advances in Climate Change Research 2024年第4期15卷 658-668页

作者： Hang SU Xin-Yue ZHONG Bin CAO Yuan-Tao HU Lei ZHENG Tingjun ZHANG State Key Laboratory of Water Resources and Hydropower Engineering Science Wuhan UniversityWuhan 430072China Key Laboratory of Remote Sensing of Gansu Province Heihe Remote Sensing Experimental Research StationNorthwest Institute of Eco-Environment and ResourcesChinese Academy of SciencesLanzhou 730000China Key Laboratory of Tibetan Plateau Earth System Environment and ResourcesNational Tibetan Plateau Data CenterInstitute of Tibetan Plateau ResearchChinese Academy of SciencesBeijing 100101China Climate Centre of Shaanxi Province Xi'an71000China School of Geospatial Engineering and Science Sun Yat-sen University and Southern Marine Science and Engineering Guangdong Laboratory(Zhuhai)Zhuhai 519082China Key Laboratory of Westem China's Environmental Systems(Ministry of Education).College of Earth and Environmental Sciences Lanzhou Universiny Lanzhou 730000China

Snow density is one of the basic properties used to describe snow cover characteristics,and it is critical for remote sensing retrieval,water resources assessment and modeling *** are many instruments available to measure snow density in ***,there are mea-surement errors of snow density for bulk and layers or gravimetric and electronic instruments,which may affect the accuracy of remote sensing retrieval and model *** in China,due to the noticeable heterogeneity of snowpacks,it is necessary to evaluate in detail the performance and applicability of snow density instruments in different snowpack *** study evaluated the performance of different snow density instruments:the Federal Sampler,the model VS-43 snow density cylinder(VS-43),the wedge snow density cutter(WC1000 and WC25O),and the Snow *** average bulk snow density of all instrument measurements was set as the reference value for *** results showed that as compared with the reference,the VS-43 cylinder presented the best performance for bulk snow density measurement in the measured range with the lowest RMSE(11 kg m^(-3)),BIAS(3 kg m^(-3)),and MRE(1.6%).For layer observation,bulk snow density was overestimated by 8.1%with WC1000 and underestimated by 11.4%with Snow Fork which was the worst performance compared with the reference value,and there were greater measurement errors of snow density in the depth hoar than other snow *** with grassland,the uncertainty of snow density measurements was slightly lower in ***,the Federal Sampler and VS-43 cylinder are more suitable for bulk snow density measurement in deep snowpack regions across China,and it is recommended to use WC1000,WC250 and Snow Fork to measure the snow density of snow layers in the snow stratigraphy.

关键词： Snowdensity Measurement Uncertainty comparison

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Significance of Convolutional Neural Network in View of Lip Reading for Speech-Impaired People

Significance of Convolutional Neural Network in View of Lip ...

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Emerging Frontiers in Electrical and Electronic Technologies (ICEFEET), International Conference on

作者： Vasundhara Sandi S. Shoba School of Electronics Engineering Vellore Institute of Technology Chennai India Center for Advanced Data Science Vellore Institute of Technology Chennai India

One way to understand what another person is saying is by lip reading. This is done by observing the speaker's lips movements and determining the words being spoken. One of the key problems with lip reading methods is visual uncertainty, making it difficult to implement in practice. When homophones have similar or nearly identical lip movement, this occurs at the word level. ([p], [m], [b] are all examples). One of the primary factors behind this effort is the intention to aid those who struggle with speech problems. Many people throughout the globe have difficulties communicating because of speech impediments, but visual speech recognition has the potential to change that. In the past ten years, as deep learning has developed, there has been a steady increase in the requirement for effective speech-to-text systems. Intelligent devices that can read lips are called lip reading systems. In order to convert lip movement into text, it is necessary to identify the face, find the lips, identify the characteristics, and then train the classifier to detect lip movement. This article presents an extensive examination of the latest advancements in deep learning model-based Visual speech recognition (VSR). The emphasis is on addressing data problems, task-specific complexities like homophones and absence of context, and providing existing results. Progress in these areas will accelerate the implementation of the silent speech interface from concept to reality. In addition to that, we also summarised the statistical results of standard datasets of VSR.

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Securing Smart Homes Using Hybrid Stacking Ensemble Deep Learning-Enabled Framework 2

Securing Smart Homes Using Hybrid Stacking Ensemble Deep Lea...

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2nd International Engineering Conference on Electrical, Energy, and Artificial Intelligence, EICEEAI 2023

作者： Moubayed, Abdallah Injadat, MohammadNoor Abdellatif Mohamed, Tamer Mohamed Almatarneh, Sattam Al-Mashagbeh, Malak Aljaidi, Mohammad School of Computing and Augmented Intelligence Arizona State University GilbertAZ United States Lebanese American University Electrical and Computer Engineering Department Byblos Lebanon Western University Electrical and Computer Engineering Department LondonON Canada Zarqa University Data Science and Artificial Intelligence Department Zarqa Jordan School of Engineering Applied Science and Technology Canadian University Dubai Dubai United Arab Emirates Zarqa University Cyber Security Department Zarqa Jordan Zarqa University Computer Science Department Zarqa Jordan

ISBN: (纸本)9798350373363

The added deployment of Internet-of-Things (IoT) devices in various environments has led to people increasingly adding this technology to their homes to make them more comfortable and have a more convenient living environment. Despite the promise and the growth potential of the smart home market, one main concern is the security and privacy of smart home devices. This is because smart home devices equipped with IoT technology are prone to various network-based attacks. With smart home devices having limited computation and memory capabilities, many IoT manufacturers often overlook the security aspect by focusing more on the functionality. To address this issue, the concepts of machine learning (ML) and deep learning (DL) have emerged as viable and promising solutions. To that end, this paper proposes a hybrid stacking ensemble DL-enabled framework that combines both classical ML models with sophisticated DL models. The goal is to make use of the simplicity and interpretability of the classical ML models with the superior time-dependent patterns that DL models can detect. The proposed framework's performance is evaluated using multiple datasets representing different smart home devices. Experimental results show that the proposed framework achieved better detection accuracy, precision, recall, and F-score. © 2023 IEEE.

关键词： Intrusion detection

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The Influence of Persistent Receipt of Job-Related Instant Messages After Work on Employees' Intention to Quit: An Empirical Study in China 25

The Influence of Persistent Receipt of Job-Related Instant M...

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25th IEEE International Conference on Computer Supported Cooperative Work in Design, CSCWD 2022

作者： Yang, Can Zhao, Yi Xuan Pak Ki Kwok, Alex Yan, Mian School of Intelligent Systems Science and Engineering Jinan University China Hong Kong Shue Yan University IFREE Group Innovation and Research Centre Department of Applied Data Science Hong Kong Institute of Physical Internet Jinan University China

ISBN: (纸本)9781665405270

Instant messaging (IM) is becoming an increasingly important component of our work. However, little is known regarding the association between employees' persistent receipt of job-related instant messages after work and their intention to quit. This is one of the early investigations of this relationship. The purpose of this study is to determine how IM applications, such as WeChat and QQ, are used outside of work hours in China, as well as its link to IM-related pressure, work-home conflict, and intention to quit the organisation. The results should shed light on whether it is prudent for businesses to communicate with their employees via IM after working hours. The study's model comprises four primary constructs: perceived abusive use of IM after work, IM-related pressure, work-home conflict, and intention to quit. A questionnaire containing the above constructs was developed. The frequency and average time of receiving information were used to quantify abusive use of IM after work. In contrast, IM-related pressure, work-home conflict, and intention to quit were measured based on validated measurement items from literature and adjusted according to this study's focus. A total of 42 valid responses were obtained from the survey and were used for data analysis. A partial least squares structural equation modelling approach was employed for parameter estimation and model testing using SmartPLS. The empirical results showed that perceived abusive use of IM after work influenced work-home conflict via IM-related pressure. However, there was no significant relationship between work-home conflict and intention to quit. © 2022 IEEE.

关键词： Personnel

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Gap minimization for knowledge sharing and transfer

The Journal of Machine Learning Research

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The Journal of Machine Learning Research 2023年第1期24卷 1282-1338页

作者： Boyu Wang Jorge A. Mendez Changjian Shui Fan Zhou Di Wu Gezheng Xu Christian Gagné Eric Eaton Department of Computer Science University of Western Ontario Computer Science & Artificial Intelligence Laboratory Massachusetts Institute of Technology Institute Intelligence and Data Université Laval School of Transportation Science and Engineering Beihang University Department of Electrical and Computer Engineering McGill University Department of Computer and Information Science University of Pennsylvania

Learning from multiple related tasks by knowledge sharing and transfer has become increasingly relevant over the last two decades. In order to successfully transfer information from one task to another, it is critical to understand the similarities and differences between the domains. In this paper, we introduce the notion of performance gap, an intuitive and novel measure of the distance between learning tasks. Unlike existing measures which are used as tools to bound the difference of expected risks between tasks (e.g., H-divergence or discrepancy distance), we theoretically show that the performance gap can be viewed as a data- and algorithm-dependent regularizer, which controls the model complexity and leads to finer guarantees. More importantly, it also provides new insights and motivates a novel principle for designing strategies for knowledge sharing and transfer: gap minimization. We instantiate this principle with two algorithms: 1. gapBoost, a novel and principled boosting algorithm that explicitly minimizes the performance gap between source and target domains for transfer learning; and 2. gapMTNN, a representation learning algorithm that reformulates gap minimization as semantic conditional matching for multitask learning. Our extensive evaluation on both transfer learning and multitask learning benchmark data sets shows that our methods outperform existing baselines.

关键词： performance gap transfer learning multitask learning regularization algorithmic stability

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ViDSOD-100: A New dataset and A Baseline Model for RGB-D Video Salient Object Detection

arXiv

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arXiv 2024年

作者： Lin, Junhao Zhu, Lei Shen, Jiaxing Fu, Huazhu Zhang, Qing Wang, Liansheng The Hong Kong University of Science and Technology Guangzhou China The Hong Kong University of Science and Technology Hong Kong School of Informatics Xiamen University China Department of Computing and Decision Sciences Lingnan University Hong Kong Singapore School of Data and Computer Science Sun Yat-Sen University China

With the rapid development of depth sensor, more and more RGB-D videos could be obtained. Identifying the foreground in RGB-D videos is a fundamental and important task. However, the existing salient object detection (SOD) works only focus on either static RGB-D images or RGB videos, ignoring the collaborating of RGB-D and video information. In this paper, we first collect a new annotated RGB-D video SOD (ViDSOD-100) dataset, which contains 100 videos within a total of 9,362 frames, acquired from diverse natural scenes. All the frames in each video are manually annotated to a high-quality saliency annotation. Moreover, we propose a new baseline model, named attentive triple-fusion network (ATF-Net), for RGB-D video salient object detection. Our method aggregates the appearance information from an input RGB image, spatio-temporal information from an estimated motion map, and the geometry information from the depth map by devising three modality-specific branches and a multi-modality integration branch. The modality-specific branches extract the representation of different inputs, while the multi-modality integration branch combines the multi-level modality-specific features by introducing the encoder feature aggregation (MEA) modules and decoder feature aggregation (MDA) modules. The experimental findings conducted on both our newly introduced ViDSOD-100 dataset and the well-established DAVSOD dataset highlight the superior performance of the proposed *** performance enhancement is demonstrated both quantitatively and qualitatively, surpassing the capabilities of current state-of-the-art techniques across various domains, including RGB-D saliency detection, video saliency detection, and video object segmentation. Our data and our code are available at ViDSOD-100. Copyright © 2024, The Authors. All rights reserved.

关键词： Video analysis

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Dynamic Key Generationfor Secure File Sharing System Using Time Stamp

Dynamic Key Generationfor Secure File Sharing System Using T...

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Innovative computing, Intelligent Communication and Smart Electrical Systems (ICSES), International Conference on

作者： S H Annie Silviya C M Hilda Jerlin N Mohana Suganthi V Helen Sathiya R Jayashree N Oviya Computer Science and Engineering Rajalakshmi Institute of Technology Chennai India Artificial Intelligence and Data Science Panimalar Engineering College Chennai India School of Computing Computer Science and Engineering Vel Tech Rangarajan Dr Sagunthala R&D Institute of Science and Technology Chennai India Computer Science and Engineering Vel Tech Multi Tech Dr Rangarajan Dr Sakunthala Engineering College Chennai India

ISBN: (数字)9798331543624

ISBN: (纸本)9798331543631

Dynamic key generation for secure file sharing system using timestamp is a novel approach to military-based secure file sharing, which adds an additional layer of security to AES-2S6 which is prominently considered as the military-grade encryption. It is designed to enhance the confidentiality and integrity of transmitted files. When a Sender uploads a file, the system prompts for a character, converts it to its ASCII value, adds the current system time value to it and utilizes this aggregated value as a unique code to secure the uploaded file. To retrieve a file, the user must possess either the file key or initiate the retrieval process during the exact time of the original upload or in the next precise clock cycle. The combination of these factors provides an extra layer of security, making unauthorized access significantly more challenging.

关键词： Hands Protocols Codes Receivers Real-time systems database systems Peer-to-peer computing Encryption ASCII Clocks

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