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Sketch-and-solve approaches to k-means clustering by semidefinite programming

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arXiv 2022年

作者： Clum, Charles Mixon, Dustin G. Villar, Soledad Xie, Kaiying Department of Mathematics The Ohio State University ColumbusOH United States Translational Data Analytics Institute The Ohio State University ColumbusOH United States Department of Applied Mathematics & Statistics Johns Hopkins University BaltimoreMD United States Mathematical Institute for Data Science Johns Hopkins University BaltimoreMD United States Department of Electrical and Computer Engineering The Ohio State University ColumbusOH United States

We introduce a sketch-and-solve approach to speed up the Peng-Wei semidefinite relaxation of k-means clustering. When the data is appropriately separated we identify the k-means optimal clustering. Otherwise, our approach provides a high-confidence lower bound on the optimal k-means value. This lower bound is data-driven;it does not make any assumption on the data nor how it is generated. We provide code and an extensive set of numerical experiments where we use this approach to certify approximate optimality of clustering solutions obtained by k-means++. © 2022, CC BY.

关键词： Optimal systems

A Novel Method to Identify and Recover the Fault Nodes over 5G Wireless Sensor Network Environment

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A Novel Method to Identify and Recover the Fault Nodes over ...

Innovation in Technology (APCIT), Asia Pacific Conference on

作者： Vijaya Vardan Reddy S P A. Hency Juliet R. Jayadurga S. Sethu Pavithra K N V. Samuthira Pandi Department of Electronics and Communication Engineering R.M.K. Engineering College Chennai Tamil Nadu India Department of Data Analytics Saveetha College of Liberal Arts and Sciences Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India Department of Master of Business Administration Prince Shri Venkateshwara Padmavathy Engineering College Chennai Tamil Nadu India Department of Computer Science and Engineering Vels Institute of Science Technology & Advanced Studies (VISTAS) Chennai Tamilnadu India Department of Science and Humanities R. M. D. Engineering College Chennai Tamil Nadu India Centre for Advanced Wireless Integrated Technology Chennai Institute of Technology Chennai Tamil Nadu India

ISBN: (数字)9798350361537

ISBN: (纸本)9798350361544

The significance of communication networks is growing in tandem with the proliferation of communication technologies. Present methods for maintaining 5G Wireless Sensor Networks are still restricted to routine maintenance and post-maintenance tasks. They lack a comprehensive function for monitoring the network's status, are unable to assess the network's health, and are difficult to maintain before the 5G-based WSNs seriously degrade. 5G Wireless Sensor Network faults can only be resolved by highly trained technicians due to low maintenance efficiency. As a result, errors cannot be detected or located promptly or accurately, leading to forced repairs that incur the expense of new network cables. First, the article lays forth the basics of network fault analysis. Then, it uses deep learning to simulate communication network problem diagnosis. Lastly, the experimental section compares various methodologies and analyses the findings of fault location. The results of the simulation demonstrate that the suggested approach mitigates the created model's flaws to a certain degree while simultaneously enhancing the network fault detection model's accuracy, universality, and robustness. A novel approach to autonomous placement, the Fault Node Recovery Protocol is described in this study. It is implemented in 5G mobile communications to detect faulty data according to wireless sensor network standards, and it is made to fix the problems with conventional techniques, such poor positioning precision and lengthy running time. The automated localization model for 5G mobile communication fault data, constructed using the suggested FNRP approach, is presented in this work. By comparing it to the standard Adhoc On-Demand Distance Vector Routing protocol, we can see how well the suggested system performs.

关键词： Wireless communication Location awareness Wireless sensor networks Accuracy 5G mobile communication Vectors Routing protocols

AI/ML-Based Automatic Modulation Recognition: Recent Trends and Future Possibilities

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arXiv 2025年

作者： Jafarigol, Elaheh Alaghband, Behnoud Gilanpour, Azadeh Hosseinipoor, Saeid Mirmozafari, Mirhamed Data Science and Analytics Institute University of Oklahoma 202 W. Boyd St. Room 409 NormanOK73019 United States Electrical and Computer Science Department University of New Mexico Farris Engineering Center 1901 RedondoMSC01 1130 AlbuquerqueNM87131 United States Labcorp Drug Development 3301 Kinsman Blvd MadisonWI53704 United States Samsung Research America San FranciscoCA94111 United States Maxwave LLC 2616 Dublin Way WaunakeeWI53597 United States

We present a review of high-performance automatic modulation recognition (AMR) models proposed in the literature to classify various Radio Frequency (RF) modulation schemes. We replicated these models and compared their performance in terms of accuracy across a range of signal-to-noise ratios. To ensure a fair comparison, we used the same dataset (RadioML-2016A), the same hardware, and a consistent definition of test accuracy as the evaluation metric, thereby providing a benchmark for future AMR studies. The hyperparameters were selected based on the authors’ suggestions in the associated references to achieve results as close as possible to the originals. The replicated models are publicly accessible for further analysis of AMR models. We also present the test accuracies of the selected models versus their number of parameters, indicating their complexities. Building on this comparative analysis, we identify strategies to enhance these models’ performance. Finally, we present potential opportunities for improvement, whether through novel architectures, data processing techniques, or training strategies, to further advance the capabilities of AMR models. © 2025, CC BY.

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Differentially private stochastic optimization: new results in convex and non-convex settings 21

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Differentially private stochastic optimization: new results ...

Proceedings of the 35th International Conference on Neural Information Processing Systems

作者： Raef Bassily Cristóbal Guzmán Michael Menart Department of Computer Science & Engineering Translational Data Analytics Institute (TDAI) The Ohio State University Department of Applied Mathematics University of Twente Inst. for Mathematical and Comput. Eng. Pontificia Universidad Católica de Chile Department of Computer Science & Engineering The Ohio State University

ISBN: (纸本)9781713845393

We study differentially private stochastic optimization in convex and non-convex settings. For the convex case, we focus on the family of non-smooth generalized linear losses (GLLs). Our algorithm for the l2 setting achieves optimal excess population risk in near-linear time, while the best known differentially private algorithms for general convex losses run in super-linear time. Our algorithm for the ℓ1 setting has nearly-optimal excess population risk $\tilde{O}\left(\sqrt{\frac{\log{d}}{n\varepsilon}}\right)$ and circumvents the dimension dependent lower bound of [AFKT21] for general non-smooth convex losses. In the differentially private non-convex setting, we provide several new algorithms for approximating stationary points of the population risk. For the ℓ1-case with smooth losses and polyhedral constraint, we provide the first nearly dimension independent rate, $\tilde O\left(\frac{\log^{2/3}{d}}{{(n\varepsilon)^{1/3}}}\right)$ in linear time. For the constrained ℓ1-case with smooth losses, we obtain a linear-time algorithm with rate $\tilde O\left(\frac{1}{n^{1/3}}+\frac{d^{1/5}}{(n\varepsilon)^{2/5}}\right)$. Finally, for the ℓ2-case we provide the first method for non-smooth weakly convex stochastic optimization with rate $\tilde O\left(\frac{1}{n^{1/4}}+\frac{d^{1/6}}{(n\varepsilon)^{1/3}}\right)$ which matches the best existing non-private algorithm when d = O(√n). We also extend all our results above for the non-convex ℓ2 setting to the ℓp setting, where 1 < p ≤ 2, with only polylogarithmic (in the dimension) overhead in the rates.

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A Brief Review of Explainable Artificial Intelligence in Healthcare

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SSRN

SSRN 2023年

作者： Sadeghi, Zahra Alizadehsani, Roohallah Cifci, Mehmet Akif Kausar, Samina Rehman, Rizwan Mahanta, Priyakshi Bora, Pranjal Kumar Almasri, Ammar Alkhawaldeh, Rami S. Hussain, Sadiq Alatas, Bilal Shoeibi, Afshin Moosaei, Hossein Hladík, Milan Nahavandi, Saeid Pardalo, Panos M. Institute for Big Data Analytics Faculty of Computer Science Dalhousie University Canada Deakin University Geelong Australia The Institute of Computer Technology Tu Wien University Vienna1040 Austria University of Kotli Azad Jammu and Kashmir Azad Kashmir Kotli Pakistan Centre for Computer Science and Applications Dibrugarh University Assam India Department of Management Information Sys Al-Balqa Applied University Salt19117 Jordan Department of Computer Information Systems The University of Jordan Aqaba77110 Jordan Examination Branch Dibrugarh University Assam Dibrugarh India Department of Software Eng. Firat University Elazig23100 Turkey Data Science and Computational Intelligence Institute University of Granada Spain Department of Informatics Faculty of Science Jan Evangelista Purkyně University in Ústí nad Labem Czech Republic Department of Applied Mathematics School of Computer Science Faculty of Mathematics and Physics Charles University Prague Czech Republic Harvard Paulson School of Engineering and Applied Sciences Harvard University AllstonMA02134 United States Swinburne University of Technology HawthornVIC3122 Australia Center for Applied Optimization Department of Industrial and Systems Engineering University of Florida Gainesville32611 United States

XAI refers to the techniques and methods for building AI applications which assist end users to interpret output and predictions of AI models. Black box AI applications in high-stakes decision-making situations, such as medical domain have increased the demand for transparency and explainability since wrong predictions may have severe consequences. Model explainability and interpretability are vital successful deployment of AI models in healthcare practices. AI applications' underlying reasoning needs to be transparent to clinicians in order to gain their trust. This paper presents a systematic review of XAI aspects and challenges in the healthcare domain. The primary goals of this study are to review various XAI methods, their challenges, and related machine learning models in healthcare. The methods are discussed under six categories: Features-oriented methods, global methods, concept models, surrogate models, local pixel-based methods, and human-centric methods. Most importantly, the paper explores XAI role in healthcare problems to clarify its necessity in safety-critical applications. The paper intends to establish a comprehensive understanding of XAI-related applications in the healthcare field by reviewing the related experimental results. To facilitate future research for filling research gaps, the importance of XAI models from different viewpoints and their limitations are investigated. © 2023, The Authors. All rights reserved.

关键词： Decision making

A high-frequency mobility big-data reveals how COVID-19 spread across professions, locations and age groups

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arXiv 2022年

作者： Zhao, Chen Zhang, Jialu Hou, Xiaoyue Yeung, Chi Ho Zeng, An College of Computer and Cyber Security Hebei Normal University Shijiazhuang050024 China Hebei Key Laboratory of Network and Information Security Shijiazhuang050024 China Hebei Provincial Engineering Research Center for Supply Chain Big Data Analytics and Data Security Shijiazhuang050024 China Department of Science and Environmental Studies The Education University of Hong Kong Hong Kong School of Systems Science Beijing Normal University Beijing100875 China

As infected and vaccinated population increases, some countries decided not to impose non-pharmaceutical intervention measures anymore and to coexist with COVID-19. However, we do not have a comprehensive understanding of its consequence, especially for China where most population has not been infected and most Omicron transmissions are silent. This paper serves as the first study to reveal the complete silent transmission dynamics of COVID-19 overlaying a big data of more than 0.7 million real individual mobility tracks without any intervention measures throughout a week in a Chinese city, with an extent of completeness and realism not attained in existing studies. Together with the empirically inferred transmission rate of COVID-19, we find surprisingly that with only 70 citizens to be infected initially, 0.33 million becomes infected silently at last. We also reveal a characteristic daily periodic pattern of the transmission dynamics, with peaks in mornings and afternoons. In addition, retailing, catering and hotel staff are more likely to get infected than other professions. Unlike all other age groups and professions, elderly and retirees are more likely to get infected at home than outside home. Copyright © 2022, The Authors. All rights reserved.

关键词： COVID-19

Exploring the Capabilities of a Language Model-Only Approach for Depression Detection in Text data

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Exploring the Capabilities of a Language Model-Only Approach...

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

作者： Misha Sadeghi Bernhard Egger Reza Agahi Robert Richer Klara Capito Lydia Helene Rupp Lena Schindler-Gmelch Matthias Berking Bjoern M. Eskofier Department Artificial Intelligence in Biomedical Engineering Machine Learning and Data Analytics Lab Friedrich-Alexander-Universität Erlangen- Nürnberg (FAU) Erlangen Germany Department of Computer Science Chair of Visual Computing Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU) Erlangen Germany Syenah GMBH Eschborn Germany Chair of Clinical Psychology and Psychotherapy Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU) Erlangen Germany

Depression is a prevalent and debilitating mental health condition that requires accurate and efficient detection for timely and effective treatment. In this study, we utilized the E-DAIC (Extended Distress Analysis Interview Corpus-Wizard-of-Oz) dataset, an extended version of the DAIC-WOZ dataset, which consists of semi-clinical interviews conducted by an animated virtual interviewer called Ellie, controlled by a human interviewer in another room. With 275 participants, the E-DAIC dataset represents a valuable resource for investigating depression detection methods. Our aim is to predict PHQ-8 scores through text analysis. Leveraging state-of-the-art speech processing, LLM-based text summarization, and a specialized depression detection module, we demonstrate the transformative potential of language data analysis in enhancing depression screening. By overcoming the limitations of manual feature extraction methods, our automated techniques provide a more efficient and effective means of evaluating depression. In our evaluation, we achieve robust accuracy on the development set of the E-DAIC dataset, with a Mean Absolute Error (MAE) of 3.65 in estimating PHQ-8 scores from recorded interviews. This remarkable performance highlights the efficacy of our approach in automatically predicting depression severity. Our research contributes to the growing evidence supporting the use of LLMs in mental health assessment, showcasing the role of innovative technologies in advancing patient care for depression.

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Multi-Path Long-Term Vessel Trajectories Forecasting with Probabilistic Feature Fusion for Problem Shifting

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arXiv 2023年

作者： Spadon, Gabriel Kumar, Jay Eden, Derek van Berkel, Josh Foster, Tom Soares, Amilcar Fablet, Ronan Matwin, Stan Pelot, Ronald Institute for Big Data Analytics Dalhousie University HalifaxNS Canada Industrial Engineering Department Dalhousie University HalifaxNS Canada Departement de Genie Mathematique et Electrique IMT Atlantique Brest France Department of Comp. Sci. and Media Tech. Linnaeus University Vaxjo Sweden Institute of Computer Science Polish Academy of Sciences Warsaw Poland DHI Water & Environment Inc. Ottawa Canada

This paper addresses the challenge of boosting the precision of multi-path long-term vessel trajectory forecasting on engineered sequences of Automatic Identification System (AIS) data using feature fusion for problem shifting. We have developed a deep auto-encoder model and a phased framework approach to predict the next 12 hours of vessel trajectories using 1 to 3 hours of AIS data as input. To this end, we fuse the spatiotemporal features from the AIS messages with probabilistic features engineered from historical AIS data referring to potential routes and destinations. As a result, we reduce the forecasting uncertainty by shifting the problem into a trajectory reconstruction problem. The probabilistic features have an F1-Score of approximately 85% and 75% for the vessel route and destination prediction, respectively. Under such circumstances, we achieved an R2 Score of over 98% with different layer structures and varying feature combinations;the high R2 Score is a natural outcome of the well-defined shipping lanes in the study region. However, our proposal stands out among competing approaches as it demonstrates the capability of complex decision-making during turnings and route selection. Furthermore, we have shown that our model achieves more accurate forecasting with average and median errors of 11km and 6km, respectively, a 25% improvement from the current state-of-the-art approaches. The resulting model from this proposal is deployed as part of a broader Decision Support System to safeguard whales by preventing the risk of vessel-whale collisions under the smartWhales initiative and acting on the Gulf of St. Lawrence in Atlantic Canada. Copyright © 2023, The Authors. All rights reserved.

关键词： data fusion

Reproducing kernel Hilbert space, Mercer's theorem, eigenfunctions, Nyström method, and use of kernels in machine learning: Tutorial and survey

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arXiv 2021年

作者： Ghojogh, Benyamin Ghodsi, Ali Karray, Fakhri Crowley, Mark Department of Electrical and Computer Engineering Machine Learning Laboratory University of Waterloo WaterlooON Canada Department of Statistics and Actuarial Science David R. Cheriton School of Computer Science Data Analytics Laboratory University of Waterloo WaterlooON Canada Department of Electrical and Computer Engineering Centre for Pattern Analysis and Machine Intelligence University of Waterloo WaterlooON Canada

This is a tutorial and survey paper on kernels, kernel methods, and related fields. We start with reviewing the history of kernels in functional analysis and machine learning. Then, Mercer kernel, Hilbert and Banach spaces, Reproducing Kernel Hilbert Space (RKHS), Mercer's theorem and its proof, frequently used kernels, kernel construction from distance metric, important classes of kernels (including bounded, integrally positive definite, universal, stationary, and characteristic kernels), kernel centering and normalization, and eigenfunctions are explained in detail. Then, we introduce types of use of kernels in machine learning including kernel methods (such as kernel support vector machines), kernel learning by semi-definite programming, Hilbert-Schmidt independence criterion, maximum mean discrepancy, kernel mean embedding, and kernel dimensionality reduction. We also cover rank and factorization of kernel matrix as well as the approximation of eigenfunctions and kernels using the Nyström method. This paper can be useful for various fields of science including machine learning, dimensionality reduction, functional analysis in mathematics, and mathematical physics in quantum mechanics. Copyright © 2021, The Authors. All rights reserved.

关键词： Functional analysis