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arXiv 2019年

作者： Chen, Feiyu Yang, Yuchen Xu, Liwei Zhang, Taiping Zhang, Yin School of Big Data and Software Engineering Chongqing University Chongqing China Department of Computational and Applied Mathematics Rice University HoustonTX United States School of Mathematical Sciences University of Electronic Science and Technology of China Chengdu Sichuan China College of Computer Science Chongqing University Chongqing China Institute for Data and Decision Analytics Chinese University of Hong Kong Shenzhen China

The K-means algorithm is arguably the most popular data clustering method, commonly applied to processed datasets in some "feature spaces", as is in spectral clustering. Highly sensitive to initializations, however, K-means encounters a scalability bottleneck with respect to the number of clusters K as this number grows in big data applications. In this work, we promote a closely related model called K-indicators model and construct an efficient, semi-convex-relaxation algorithm that requires no randomized initializations. We present extensive empirical results to show advantages of the new algorithm when K is large. In particular, using the new algorithm to start the K-means algorithm, without any replication, can significantly outperform the standard K-means with a large number of currently state-ofthe-A rt random replications. Copyright © 2019, The Authors. All rights reserved.

关键词： K-means clustering

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Reproducibility and Replicability in Neuroimaging: Constrained IVA as an Effective Assessment Tool

Reproducibility and Replicability in Neuroimaging: Constrain...

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European Signal Processing Conference (EUSIPCO)

作者： Francisco Laport Adriana Dapena Trung Vu Hanlu Yang Vince Calhoun Tülay Adali Department of Computer Science and Electrical Engineering University of Maryland Baltimore County MD USA CITIC Research Center University of A Coruña A Coruña Spain Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS) Georgia State University Georgia Institute of Technology and Emory University Atlanta GA USA

ISBN: (数字)9789464593617

ISBN: (纸本)9798331519773

Matrix decomposition techniques have been successfully applied in the analysis of multi-subject functional magnetic resonance imaging (fMRI) data. These data-driven approaches that assume the linear blind source separation (BSS) problem can yield an unsupervised and fully interpretable solution when there is a good model match. However, selecting a suitable model order that provides an accurate model match is an important challenge. Replicability and computational reproducibility are two key aspects that are also intimately related to interpretability. Despite clear evidence that solutions with poor reproducibility can lead to suboptimal results, the evaluation of reproducibility in matrix decomposition techniques remains limited in the existing literature. We propose the use of constrained independent vector analysis (cIVA), a state-of-the-art joint BSS technique, to assess the influence of model order selection for replicability and reproducibility. We demonstrate the attractiveness of clVA for replicability by alleviating permutation ambiguity as well as enabling additional quantification opportunities. Our results show that highly reproducible model orders achieve a good model match with highly interpretable and replicable solutions when clVA is applied to four different restina-state fMRI datasets.

关键词： Analytical models Computational modeling Europe Functional magnetic resonance imaging Signal processing Reproducibility of results Vectors Blind source separation Matrix decomposition Replicability

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Image Fusion in Remote Sensing: An Overview and Meta-Analysis

SSRN

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SSRN 2024年

作者： Albanwan, Hessah Qin, Rongjun Tang, Yang Geospatial Data Analytics Laboratory The Ohio State University 218B Bolz Hall 2036 Neil Avenue ColumbusOH43210 United States Department of Civil Environmental and Geodetic Engineering The Ohio State University 218B Bolz Hall 2036 Neil Avenue ColumbusOH43210 United States Department of Electrical and Computer Engineering The Ohio State University 205 Dreese Lab 2036 Neil Avenue ColumbusOH43210 United States Translational Data Analytics Institute The Ohio State University United States

Image fusion in Remote Sensing (RS) has been a consistent demand due to its ability to turn raw images of different resolutions, sources, and modalities into accurate, complete, and spatio-temporally coherent images. It greatly facilitates downstream applications such as pan-sharpening, change detection, land-cover classification, etc. Yet, image fusion solutions are highly disparate to various remote sensing problems and thus are often narrowly defined in existing reviews as topical applications, such as pan-sharpening and spatial-temporal image fusion. Considering that image fusion can be theoretically applied to any gridded data through pixel-level operations, in this paper, we expand its scope by comprehensively surveying relevant works with a simple taxonomy: 1) many-to-one image fusion;2) many-to-many image fusion. This simple taxonomy defines image fusion as a mapping problem that turns either a single or a set of images into another single or set of images, depending on the desired coherence, e.g., spectral, spatial/resolution coherence, etc. We show that this simple taxonomy, despite the significant modality difference it covers, can be presented by a conceptually easy framework. Furthermore, we provide a meta-analysis to review the major papers studying the various types of image fusion and their applications over the years (from the 1980s to date), covering 10,420 peer-reviewed papers. Finally, we discuss image fusion's main benefits and emerging challenges to provide open research directions and potential future works. © 2024, The Authors. All rights reserved.

关键词： Image fusion

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A Comparative Study on Deep-Learning Methods for Dense Image Matching of Multi-angle and Multi-date Remote Sensing Stereo Images

arXiv

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arXiv 2022年

作者： Albanwan, Hessah Qin, Rongjun Geospatial Data Analytics Laboratory The Ohio State University 218B Bolz Hall 2036 Neil Avenue ColumbusOH43210 United States Department of Civil Environmental and Geodetic Engineering The Ohio State University 218B Bolz Hall 2036 Neil Avenue ColumbusOH43210 United States Department of Electrical and Computer Engineering The Ohio State University 205 Dreese Lab 2036 Neil Avenue ColumbusOH43210 United States Translational Data Analytics Institute The Ohio State University United States

Deep learning (DL) stereo matching methods gained great attention in remote sensing satellite datasets. However, most of these existing studies conclude assessments based only on a few/single stereo images lacking a systematic evaluation on how robust DL methods are on satellite stereo images with varying radiometric and geometric configurations. This paper provides an evaluation of four DL stereo matching methods through hundreds of multi-date multi-site satellite stereo pairs with varying geometric configurations, against the traditional well-practiced Census-SGM (Semi-global matching), to comprehensively understand their accuracy, robustness, generalization capabilities, and their practical potential. The DL methods include a learning-based cost metric through convolutional neural networks (MC-CNN) followed by SGM, and three end-to-end (E2E) learning models using Geometry and Context Network (GCNet), Pyramid Stereo Matching Network (PSMNet), and LEAStereo. Our experiments show that E2E algorithms can achieve upper limits of geometric accuracies, while may not generalize well for unseen data. The learning-based cost metric and Census-SGM are rather robust and can consistently achieve acceptable results. All DL algorithms are robust to geometric configurations of stereo pairs and are less sensitive in comparison to the Census-SGM, while learning-based cost metrics can generalize on satellite images when trained on different datasets (airborne or ground-view). Copyright © 2022, The Authors. All rights reserved.

关键词： Satellites

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A security framework for 5G-enabled wireless sensor networks based on blockchain technology

A security framework for 5G-enabled wireless sensor networks...

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Knowledge engineering and Communication Systems (ICKECS), International Conference on

作者： M S P Durgarao Dinesh Valluru S. Saraswathi P. Malathi K. Thilagam S. Loganathan Department of Computer Science & Engineering Madanapalle Institute of Technology & Science Madanapalle Andhra Pradesh India Department of Information Technology MLRITM Engineering CollegeHyderabad Dept of CSE - AIMLVel Tech High Tech Dr Rangarajan Dr Sakuntala Engineering College Chennai Tamil Nadu India Department of Data Analytics Saveetha College of Liberal Arts and Sciences SIMATS Chennai Tamilnadu India Department of ECE Velammal Engineering College Chennai Tamil Nadu India Department of ECE Paavai Engineering College Namakkal Tamilnadu India

ISBN: (数字)9798350359688

ISBN: (纸本)9798350359695

A novel blockchain-based security architecture is presented in this study as a solution to the difficulties associated with securing 5 G -enabled WSNs. To manage the uncertain and limited resources inherent in WSNs, the system employs cryptographic techniques, intelligent contracts, and decentralized consensus protocols. The findings of this exhaustive analysis indicate that the proposed approach significantly enhances the overall security stance of 5 G -enabled WSNs. This solution to reduce security risks and vulnerabilities in WSNs is practical and contributes to the growing body of knowledge regarding secure communication in the 5 G era. Subsequent investigations and practical implementations that integrate blockchain technology with 5G-enabled WSNs are made possible by these results.

关键词： Knowledge engineering Wireless sensor networks Scalability data integrity Network security Security Resource management

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Integration of AI and Cloud-Native Technologies for Personalized Mobile Banking Experiences

Integration of AI and Cloud-Native Technologies for Personal...

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Recent Innovation in Smart and Sustainable Technology (ICRISST), International Conference on

作者： N. Legapriyadharshini T. Thirumalaikumari K. Senbagam R. Sarasu H. Anwer Basha P. Malathi Department of Computer Application Saveetha College of Liberal Arts and Sciences Saveetha Institute of Medical and Technical Sciences Chennai Tamil Nadu India Department of Computer Science Saveetha College of Liberal Arts and Sciences SIMATS Deemed to be University Chennai Tamil Nadu India Department of Computer Science and Engineering Dhanalakshmi Srinivasan College of Engineering and Technology Mamallapuram Tamil Nadu India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai Tamil Nadu India Department of Computer Science Saveetha College of Liberal Arts and Sciences Saveetha Institute of Medical and Technical Sciences Chennai Tamil Nadu India Department of Data Analytics Saveetha College of Liberal Arts and Sciences Saveetha Institute of Medical And Technical Sciences Chennai Tamil Nadu India

ISBN: (数字)9798350309140

ISBN: (纸本)9798350309157

In today's rapidly evolving digital landscape, using Artificial Intelligence (AI) and Cloud-Native Technologies is essential for improving personalized mobile banking experiences. The limitations and errors of existing financial systems necessitate such integration. Existing systems often struggle to adapt to modern consumers dynamic tastes and expectations, resulting in unsatisfactory customer experiences. Identifying the gap, the proposed system revolutionizes mobile banking applications by leveraging AI algorithms such as machine learning and natural language processing within a microservices-based cloud infrastructure. As a result, the proposed system solves the limitations of outdated architectures by providing personalized and efficient services while addressing security issues. The study indicates considerable gains in terms of user pleasure, system scalability, and security robustness through an in-depth evaluation of the results. The results highlight AI's and Cloud-Native Technologies transformational potential in revolutionizing the landscape of tailored mobile banking experiences. The study provides helpful information to financial institutions looking to adopt innovation and fulfill the evolving needs of modern consumers.

关键词： Technological innovation Scalability Microservice architectures Banking Predictive models User experience Robustness Natural language processing Security Artificial intelligence

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Machine Learning Approaches for Proactive Phishing Attack Detection

Machine Learning Approaches for Proactive Phishing Attack De...

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International Conference on Circuit, Power and Computing Technologies (ICCPCT)

作者： B. Geetha P. Malathi T. Thirumalaikumari V. Janakiraman H. Anwer Basha S. Rukmani Devi Department of Computer Science Saveetha College of Liberal Arts and Sciences Saveetha Institute of Medical and Technical Sciences Chennai Tamil Nadu India Department of Data Analytics Saveetha College of Liberal Arts and Sciences Saveetha Institute of Medical and Technical Sciences Chennai Tamil Nadu India Department of ECE Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai Tamil Nadu India

ISBN: (数字)9798350372816

ISBN: (纸本)9798350372823

Phishing attacks remain a serious problem in cybersecurity, necessitating advances in detection methods. The study describes a novel method for detecting phishing attacks that use advanced machine learning (ML) techniques, notably convolutional neural networks (CNNs) and recurrent neural networks (RNNs). CNNs and RNNs were chosen because of their ability to detect spatial and sequential patterns in phishing data. The system achieves improved performance by iterative model refinement and ensemble learning, beating existing rule-based systems in terms of precision (96%), recall (94%), and F1-score (95%). Furthermore, the proposed strategy has significantly lower false positive (3.8%) and false negative (5.7%) rates than existing systems, indicating greater dependability in distinguishing between legitimate and malicious emails. Cross-validation validates that the proposed system is stable and ready for real-world use. As a result, the proposed ML-driven strategy represents a substantial leap in phishing detection, providing increased accuracy (F1-score: 95%) and resilience to emerging cybersecurity threats.

关键词： Training Recurrent neural networks Accuracy Systematics Phishing Computational modeling Training data

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Development and Innovations in Wearable Sensors and Devices for Human Machine Interaction in Virtual Platform

Development and Innovations in Wearable Sensors and Devices ...

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Augmented Intelligence and Sustainable Systems (ICAISS), International Conference on

作者： Rahul Singh Anand Javali S. Priya B Vishnu Vardhana Naidu Gaurav Gautam PGT Physics Department of Physics Presidium Gurgaon Sec-57 Haryana India Data Analytics and Innovations Dept Conduent Technology Services – USA FRISCO TEXAS USA Department of Computer Science and Engineering Nehru Institute of Engineering and Technology Coimbatore Tamilnadu India Department of Mechanical Engineering Mohan Babu University Tirupati Andhra Pradesh India Department of Physics Graphic Era Hill University Dehradun India

Recent advances in deep learning algorithms and wearable sensor technology have opened the door for creative methods to improve human-machine interaction (HMI) on virtual platforms. The use of wearable sensors and devices in conjunction with hybrid deep learning algorithms is presented in detail in this abstract. Real-time physiological and contextual data from users can be collected with the help of wearable sensors integrated into devices like smartwatches, augmented reality goggles, and biometric monitoring equipment. This information can then be used to improve augmented reality (AR) and virtual reality (VR) systems, enabling more effortless and intuitive user interactions. A seamless connection between the user and the virtual platform is made possible by the combination of sensor data and deep learning models, which enables the creation of complex algorithms that can decipher user intent, gestures, emotions, and physical actions. These hybrid models may efficiently learn complicated patterns from wearable sensor data by combining standard feature extraction techniques with convolutional neural networks (CNNs), recurrent neural networks (RNNs), and generative adversarial networks (GANs). Wearable sensors and hybrid deep learning techniques have numerous and significant applications in virtual platforms. They make it possible for, among other things, real-time activity recognition for individualized virtual fitness coaching, emotion recognition for adaptive and sympathetic virtual characters, and gesture recognition for intuitive control of virtual objects. In summary, combining wearable sensors and devices with hybrid deep learning algorithms has enormous potential to change how humans and machines interact on virtual platforms.

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G2Retro as a Two-Step Graph Generative Models for Retrosynthesis Prediction

arXiv

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arXiv 2022年

作者： Chen, Ziqi Ayinde, Oluwatosin R. Fuchs, James R. Sun, Huan Ning, Xia Computer Science and Engineering The Ohio State University ColumbusOH43210 United States Medicinal Chemistry and Pharmacognosy College of Pharmacy The Ohio State University ColumbusOH43210 United States Translational Data Analytics Institute The Ohio State University ColumbusOH43210 United States Biomedical Informatics The Ohio State University ColumbusOH43210 United States

Retrosynthesis is a procedure where a target molecule is transformed into potential reactants and thus the synthesis routes can be identified. Recently, computational approaches have been developed to accelerate the design of synthesis routes. In this paper, we develop a generative framework G2Retro for one-step retrosynthesis prediction. G2Retro imitates the reversed logic of synthetic reactions. It first predicts the reaction centers in the target molecules (products), identifies the synthons needed to assemble the products, and transforms these synthons into reactants. G2Retro defines a comprehensive set of reaction center types, and learns from the molecular graphs of the products to predict potential reaction centers. To complete synthons into reactants, G2Retro considers all the involved synthon structures and the product structures to identify the optimal completion paths, and accordingly attaches small substructures sequentially to the synthons. Here we show that G2Retro is able to better predict the reactants for given products in the benchmark dataset than the state-of-the-art methods. © 2022, CC BY-NC-SA.

关键词： Forecasting

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Automation Through Machine Learning: Reinvigorating Cyber Security

Automation Through Machine Learning: Reinvigorating Cyber Se...

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International Conference on Computing and Networking Technology (ICCNT)

作者： Jayashree V. Bagade Sukhvinder Singh Dari Rekha Dhivrani Santosh Chowhan G. Ravivarman V Janakiraman Department of Information Technology Vishwakarma Institute of Technology Pune india Symbiosis Law School Nagpur Symbiosis International Pune India Department of Computer Science & Engineering Vivekananda Global University Jaipur India Department of Data Analytics and Mathematical Sciences Jain (Deemed to be University) Bangalore Karnataka India Department of Electrical and Electronics Engineering Karpagam Academy of Higher Education Coimbatore Department of Mechanical Engineering Prince Shri Venkateshwara Padmavathy Engineering College Chennai

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

Cyber security is predicated heavily on manual efforts to fight malicious assaults, but such processes are increasingly turning into overwhelmed by means of the quantity of threats - a hassle worsened in the course of the global COVID-19 pandemic. This has highlighted the importance of leveraging device studying (ML) to automate cyber protection methods. In this article, we speak how ML may be used to optimize the detection, prevention, and remediations of malicious assaults in an extensive kind of applications and offerings. We discover the various procedures being evolved that may assist make the identity of threats greater accurate and greater effortlessly to be had. We additionally recollect the capacity challenges associated with the software of ML strategies, along with the potential for unfair bias in opposition to positive instructions of customers, and become aware of methods for addressing those issues. Our end emphasizes the significance of leveraging ML in order to enable automation in cyber safety, as well as to make sure the detection of state-of-the-art new threats.

关键词： Automation Pandemics Prevention and mitigation Manuals Machine learning Turning Software Security Protection computer crime

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