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arXiv 2024年

作者： Su, Bowen You, Juntao Cai, HanQin Huang, Longxiu Department of Mathematics Michigan State Univeristy East LansingMI48824 United States Institute for Advanced Study Shenzhen University Shenzhen518000 China Department of Statistics and Data Science Department of Computer Science University of Central Florida OrlandoFL32816 United States Department of Computational Mathematics Science and Engineering Department of Mathematics Michigan State University East LansingMI48824 United States

While Bernoulli sampling is extensively studied in tensor completion, t-CUR sampling approximates low-tubal-rank tensors via lateral and horizontal subtensors. However, both methods lack sufficient flexibility for diverse practical applications. To address this, we introduce Tensor Cross-Concentrated Sampling (t-CCS), a novel and straightforward sampling model that advances the matrix cross-concentrated sampling concept within a tensor framework. t-CCS effectively bridges the gap between Bernoulli and t-CUR sampling, offering additional flexibility that can lead to computational savings in various contexts. A key aspect of our work is the comprehensive theoretical analysis provided. We establish a sufficient condition for the successful recovery of a low-rank tensor from its t-CCS samples. In support of this, we also develop a theoretical framework validating the feasibility of t-CUR via uniform random sampling and conduct a detailed theoretical sampling complexity analysis for tensor completion problems utilizing the general Bernoulli sampling model. Moreover, we introduce an efficient non-convex algorithm, the Iterative t-CUR Tensor Completion (ITCURTC) algorithm, specifically designed to tackle the t-CCS-based tensor completion. We have intensively tested and validated the effectiveness of the t-CCS model and the ITCURTC algorithm across both synthetic and real-world datasets. Copyright © 2024, The Authors. All rights reserved.

关键词： Tensors

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Early Stage Design of a mHealth Intervention for Managing Gestational Diabetes Mellitus in Bangladeshi Women 25th

Early Stage Design of a mHealth Intervention for Managing...

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25th International Conference on Human-Computer Interaction, HCII 2023

作者： Islam, Ashraful Ahmed, Eshtiak Islam, Md Rakibul EMPATHY Lab Center for Computational and Data Sciences Independent University Bangladesh Dhaka Bangladesh Department of Computer Science and Engineering Independent University Bangladesh Dhaka Bangladesh Faculty of Information Technology and Communication Sciences Tampere University Tampere Finland Computer Science Department University of Wisconsin - Eau Claire Eau ClaireWI United States

ISBN: (纸本)9783031492112

Gestational Diabetes Mellitus (GDM) is a rising concern worldwide, particularly in low-resource countries such as Bangladesh, where access to healthcare facilities is limited and awareness of GDM management is inadequate. This work proposes an early-stage design of user interfaces (UIs) for a mobile health (mHealth) intervention using the Bengali language that allows users to input daily measures of blood glucose, weight, and other GDM-related health data. The intervention is designed to provide users with personalized feedback based on their input and the necessary coaching for managing GDM. It also reminds users to take medication if prescribed by their doctors. The app has six key features: blood glucose tracking, food logging, medication reminders, activity tracking, educational resources, and personalized recommendations. Overall, this work provides a valuable contribution to the field of mHealth-based maternal and child health in Bangladesh. However, further research is necessary to evaluate the effectiveness and feasibility of the intervention in real-world scenarios. Such evaluations will provide valuable insights into its potential impact and help refine the design to better serve the needs of GDM patients in Bangladesh. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： mHealth

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Decoding Speech Categorization using Microstate Cortical EEG Signals and Machine Learning

Decoding Speech Categorization using Microstate Cortical EEG...

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IEEE Signal Processing in Medicine and Biology Symposium (SPMB)

作者： M. Mahmud M. Hasan M. Yeasin G. Bidelman Div. of General Internal Medicine Univ. of Tennessee Health Science Center Memphis TN USA Department of Electrical and Computer Engineering University of Memphis TN USA Computational and Data Science Middle Tennessee State University Murfreesboro TN USA Department of Speech Language and Hearing Sciences Indiana University Bloomington IN USA

ISBN: (数字)9798350388572

ISBN: (纸本)9798350388589

Categorical perception (CP) is a perceptual phenomenon that refers to the tendency of humans to group speech sounds into discrete units. In this work, we used cortical event-related potential (ERP) signals, recorded during a speech identification task, as input to a Hierarchical Dirichlet Process Hidden Markov Model (HDP-HMM) and machine learning (ML) classifiers to examine how neural signatures of the brain distinguish prototypical vs. ambiguous speech token categories. Particularly, we used extreme gradient boosting (XGBoost), support vector machine, and random forest classifiers. Our analysis shows that using source reconstructed whole-brain data, the XGBoost classifier yielded the best classification accuracy of 94.12% with (area under the curve (AUC) 94.13%, Precision 94.00%, F1-score 94.00%) in the 197-258 ms time window after the stimulus onset. We also identified 15 important brain regions that distinguished vowel classes with accuracy of 90.28% (AUC 90.17%, F1-score 90.00%, Precision 90.00%), 4% less than whole-brain data. Remarkably, out of these 15 critical brain regions, nine were from the left hemisphere, consistent with the left-brain dominance for language processing. The best speech decoding was obtained from early portions of the ERP (~180-320 ms), which corroborates notions that sensory encoding is critical for successful phonetic processing.

关键词： Support vector machines Accuracy Hidden Markov models Phonetics Signal processing Electroencephalography Real-time systems Decoding Speech processing Random forests

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Deep Learning Approaches for Improving English Listening Comprehension in E-Learning Environments for Adult Learners

Deep Learning Approaches for Improving English Listening Com...

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International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC)

作者： Divya Nimma V. Arunadevi Mehul Manu K S Punithaasree Fredrick Ruban Alphonse Computational Science University of Southern Mississippi Data Analyst in UMMC USA Department of Science and Humanities (SS) - English Dr.Mahalingam College of Engineering and Technology Pollachi India Allied Science (Physics) Graphic Era Hill University Bhimtal Graphic Era Deemed to be University Dehradun Uttarakhand India Department of English Nandha Engineering College (Autonomous) Erode India Department of Languages Institute of Finance and International Management Bengaluru India

ISBN: (数字)9798350376425

ISBN: (纸本)9798350376432

This research aims at trying to fill this gap in enhancing English listening comprehension of adult learners online learning which needs are not met by traditional approaches. The proposed framework is based on transformer models with a special focus on BERT, which can grasp deep linguistic features and context dependencies. The proposed work builds an application of the BERT model for cup size estimation using the Kaggle dataset TED-LIUM Corpus that is aimed at TED Talk transcripts. The following dataset offers all levels of English but contains much contextual information, which is very suitable for enhancing one’s listening ability. The model is trained to produce accurate comprehension assessments and to provide learner-centered feedback to help the learner to progress through tasks that cater to his/her abilities and interests. This approach is useful for e-learning platforms that use BERT to provide context-aware support and targeted practice in contrast to the previously established approach. Thus, the transformer-based framework is effective in reducing the mentioned gap by understanding advanced linguistic properties and offering pinpointing feedback. Based on the findings of this research suggest that a simple BERT-based system offers better accuracy compared to conventional techniques to tackle English listening-comprehension problems and to provide precisely targeted learning. From this research, deep learning models may be developed to design versatile learning environments to enhance listening comprehension for the adult learners in the digital learning environments. Consequently, the results point to the potential of effectively incorporating such ultramodern technologies into e-learning systems.

关键词： Deep learning Electronic learning Accuracy Estimation Bidirectional control Linguistics Transformers Encoding Context modeling

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Combining Self-Supervised Learning and Adversarial Training Based Domain Adaptation for Speaker Verification

Combining Self-Supervised Learning and Adversarial Training ...

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International Symposium on Chinese Spoken Language Processing

作者： Zhengyang Chen Shuai Wang Bing Han Yanmin Qian Department of Computer Science and Engineering Auditory Cognition and Computational Acoustics Lab MoE Key Lab of Artificial Intelligence AI Institute Shanghai Jiao Tong University Shanghai Shenzhen Research Institute of Big Data Shenzhen Chinese University of Hong Kong Shenzhen

ISBN: (数字)9798331516826

ISBN: (纸本)9798331516833

Adapting an existing well-trained system to a new domain using only unlabeled data is a highly sought-after yet challenging task for speaker verification in real-world scenarios. In this paper, we study two different domain adaptation methods, the adversarial domain adaptation (ADA) and the self-supervised learning-based domain adaptation (SSDA). To facilitate the deployment of unsupervised adaptation methods in applications, we conduct a detailed analysis of the characteristics of both the ADA and SSDA adaptation strategies. Our findings indicate that the SSDA strategy's performance is highly influenced by the amount of target domain data, whereas the ADA strategy is relatively insensitive to data quantity. Furthermore, augmenting target domain data enhances SSDA system performance but diminishes ADA performance. To further enhance system performance, we explore the complementarity between ADA and SSDA. Our results demonstrate that ADA and SSDA complement each other. When both strategies are applied jointly, the best system achieves over 20.0% relative Equal Error Rate (EER) improvement on the Cnceleb evaluation set and over 35.0% relative average EER improvement on the SRE16 Cantonese and Tagalog evaluation set under domain mismatched conditions.

关键词： Training Error analysis System performance Self-supervised learning Speech processing

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On computing probabilistic explanations for decision trees 22

On computing probabilistic explanations for decision trees

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Proceedings of the 36th International Conference on Neural Information Processing Systems

作者： Marcelo Arenas Pablo Barceló Miguel Romero Bernardo Subercaseaux Department of Computer Science PUC Chile and Institute for Mathematical and Computational Engineering PUC Chile and Millenium Institute for Foundational Research on Data Chile Institute for Mathematical and Computational Engineering PUC Chile and Millenium Institute for Foundational Research on Data Chile and CENIA Chile Faculty of Engineering and Science UAI Chile and CENIA Chile Carnegie Mellon University Pittsburgh

ISBN: (纸本)9781713871088

Formal XAI (explainable AI) is a growing area that focuses on computing explanations with mathematical guarantees for the decisions made by ML models. Inside formal XAI, one of the most studied cases is that of explaining the choices taken by decision trees, as they are traditionally deemed as one of the most interpretable classes of models. Recent work has focused on studying the computation of sufficient reasons, a kind of explanation in which given a decision tree T and an instance x, one explains the decision T(x) by providing a subset y of the features of x such that for any other instance z compatible with y, it holds that T (z) = T (x), intuitively meaning that the features in y are already enough to fully justify the classification of x by T. It has been argued, however, that sufficient reasons constitute a restrictive notion of explanation. For such a reason, the community has started to study their probabilistic counterpart, in which one requires that the probability of T(z) = T(x) must be at least some value δ ∈ (0, 1], where z is a random instance that is compatible with y. Our paper settles the computational complexity of δ-sufficient-reasons over decision trees, showing that both (1) finding δ-sufficient-reasons that are minimal in size, and (2) finding δ-sufficient-reasons that are minimal inclusion-wise, do not admit polynomial-time algorithms (unless PTIME = NP). This is in stark contrast with the deterministic case (δ =1) where inclusion-wise minimal sufficient-reasons are easy to compute. By doing this, we answer two open problems originally raised by Izza et al., and extend the hardness of explanations for Boolean circuits presented by Wäldchen et al. to the more restricted case of decision trees. On the positive side, we identify structural restrictions of decision trees that make the problem tractable, and show how SAT solvers might be able to tackle these problems in practical settings.

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Robust Tensor CUR Decompositions: Rapid Low-Tucker-Rank Tensor Recovery with Sparse Corruption

arXiv

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arXiv 2023年

作者： Cai, HanQin Chao, Zehan Huang, Longxiu Needell, Deanna Department of Statistics and Data Science Department of Computer Science University of Central Florida OrlandoFL32816 United States Department of Mathematics University of California Los Angeles Los AngelesCA90095 United States Department of Computational Mathematics Science and Engineering Department of Mathematics Michigan State University East LansingMI48823 United States

We study the tensor robust principal component analysis (TRPCA) problem, a tensorial extension of matrix robust principal component analysis (RPCA), that aims to split the given tensor into an underlying low-rank component and a sparse outlier component. This work proposes a fast algorithm, called Robust Tensor CUR Decompositions (RTCUR), for large-scale non-convex TRPCA problems under the Tucker rank setting. RTCUR is developed within a framework of alternating projections that projects between the set of low-rank tensors and the set of sparse tensors. We utilize the recently developed tensor CUR decomposition to substantially reduce the computational complexity in each projection. In addition, we develop four variants of RTCUR for different application settings. We demonstrate the effectiveness and computational advantages of RTCUR against state-of-the-art methods on both synthetic and real-world *** Codes 68p20, 68W20, 68W25, 68Q25, 65F30 Copyright © 2023, The Authors. All rights reserved.

关键词： Tensors

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Position: Bayesian Deep Learning is Needed in the Age of Large-Scale AI 41

Position: Bayesian Deep Learning is Needed in the Age of Lar...

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41st International Conference on Machine Learning, ICML 2024

作者： Papamarkou, Theodore Skoularidou, Maria Palla, Konstantina Aitchison, Laurence Arbel, Julyan Dunson, David Filippone, Maurizio Fortuin, Vincent Hennig, Philipp Hernández-Lobato, José Miguel Hubin, Aliaksandr Immer, Alexander Karaletsos, Theofanis Khan, Mohammad Emtiyaz Kristiadi, Agustinus Li, Yingzhen Mandt, Stephan Nemeth, Christopher Osborne, Michael A. Rudner, Tim G.J. Rügamer, David Teh, Yee Whye Welling, Max Wilson, Andrew Gordon Zhang, Ruqi Department of Mathematics The University of Manchester Manchester United Kingdom Eric and Wendy Schmidt Center Broad Institute of MIT and Harvard Cambridge United States Spotify London United Kingdom Computational Neuroscience Unit University of Bristol Bristol United Kingdom Centre Inria de l'Université Grenoble Alpes Grenoble France Department of Statistical Science Duke University United States Statistics Program KAUST Saudi Arabia Helmholtz AI Munich Germany Department of Computer Science Technical University of Munich Munich Germany Munich Center for Machine Learning Munich Germany Tübingen AI Center University of Tübingen Tübingen Germany Department of Engineering University of Cambridge Cambridge United Kingdom Department of Mathematics University of Oslo Oslo Norway Bioinformatics and Applied Statistics Norwegian University of Life Sciences Ås Norway Department of Computer Science ETH Zurich Switzerland Chan Zuckerberg Initiative CA United States Center for Advanced Intelligence Project RIKEN Tokyo Japan Vector Institute Toronto Canada Department of Computing Imperial College London London United Kingdom Department of Computer Science UC Irvine Irvine United States Department of Mathematics and Statistics Lancaster University Lancaster United Kingdom Department of Engineering Science University of Oxford Oxford United Kingdom Center for Data Science New York University New York United States Department of Statistics LMU Munich Munich Germany DeepMind London United Kingdom Department of Statistics University of Oxford Oxford United Kingdom Informatics Institute University of Amsterdam Amsterdam Netherlands Courant Institute of Mathematical Sciences Center for Data Science Computer Science Department New York University New York United States Department of Computer Science Purdue University West Lafayette United States

In the current landscape of deep learning research, there is a predominant emphasis on achieving high predictive accuracy in supervised tasks involving large image and language datasets. However, a broader perspective reveals a multitude of overlooked metrics, tasks, and data types, such as uncertainty, active and continual learning, and scientific data, that demand attention. Bayesian deep learning (BDL) constitutes a promising avenue, offering advantages across these diverse settings. This paper posits that BDL can elevate the capabilities of deep learning. It revisits the strengths of BDL, acknowledges existing challenges, and highlights some exciting research avenues aimed at addressing these obstacles. Looking ahead, the discussion focuses on possible ways to combine large-scale foundation models with BDL to unlock their full potential. Copyright 2024 by the author(s)

关键词： Contrastive Learning

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Optimizing Network Management and Virtualization Using Machine Learning Approach: Network Slice Prediction

Optimizing Network Management and Virtualization Using Machi...

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science, engineering and Business for Sustainable Development Goals (SEB-SDG), International Conference on

作者： Lateef Adesola Akinyemi Olamide Peter Oshinuga Sunday Oladayo Oladejo Stephen Obono Ekwe Mbuyu Sumbwanyanbe Ernest Mnkandla Oluwagbemiga Omotayo Shoewu Departments of Computer Science and Elect. Engineering CSET University of South Africa Johannesburg South Africa Department of Electronic and Computer Engineering Lagos State University Lagos Nigeria School for Data Science and Computational Thinking Stellenbosch University Stellenbosch South Africa Department of Elect. Electronic and Computer Engineering Cape Peninsula University of Technology Cape Town South Africa Department of Electrical Engineering CSET University of South Africa Johannesburg South Africa Department for Computer Science CSET University of South Africa Johannesburg South Africa

ISBN: (数字)9798350358155

ISBN: (纸本)9798350358162

This study demonstrates the utilization of Machine Learning (ML) for network slice prediction, enabling the optimization of resources for diverse network slices. Traditional methods for network slice prediction often lack efficiency and result in inaccuracies. By leveraging ML algorithms such as Naive Bayes and Random Forest, an intelligent framework that automates network slice prediction is developed. This framework enhances network virtualization and management, facilitating resource allocation. The ML algorithms take real-time network conditions and usages, such as packet delay and smart city, as input and output for selecting the most suitable network slice. data analysis is further conducted to reveal the connections between the input parameters and how these parameters influence the selection of the accurate network slice. Network slicing plays a crucial role as it enables the customization of services and facilitates efficient scaling to meet the specific needs of different applications and industries. The accuracy scores of the employed ML algorithms were generally perfect, except for the KNN and SVM classifiers, which achieved an accuracy of 94.30% and 92.16%, respectively, for the prediction of network slices based on incoming network connections and usages.

关键词： Support vector machines Machine learning algorithms Accuracy data analysis Smart cities Network slicing Prediction algorithms

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Enhancing Security and Efficiency in IoT-Enabled Smart Grids with Blockchain SDN Bi-GRU and Digital Twin

Enhancing Security and Efficiency in IoT-Enabled Smart Grids...

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International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC)

作者： Divya Nimma Soumya Patil Rahul Chauhan Shamim Ahmad Khan V. Kanpur Rani S Muthuperumal Computational Science University of Southern Mississippi Data Analyst in UMMC USA Department of CSE Dayananda Sagar College of Engineering Bangalore India Department of CSE Graphic Era Hill University Dehradun Graphic Era Deemed to be University Dehradun Uttarakhand India Glocal School of Science & Technology Glocal University U.P India Department of Electrical and Electronics Engineering Hindustan Institute of Technology and Science Padur Chennai India Department of Mathematics K. Ramakrishnan College of Engineering Trichy India

ISBN: (数字)9798350376425

ISBN: (纸本)9798350376432

The increasing connection and integration of smart grid systems through IoT for energy management and conservation face several challenges related to security, functionality, and real-time performance. Current and previous solutions addressing the requirements of smart grids often lack comprehensive approaches, focusing only on specific aspects of the grid. Existing security solutions are largely ineffective against modern cyber threats, with traditional algorithms struggling to maintain data processing speeds and anomaly detection. To address these limitations, this study proposes the following solutions: the secure integration of Blockchain with Software Defined Networking (SDN); the use of a Bi-GRU deep learning model for effective time series forecasting; and the incorporation of Real-Time Digital Twin Technology. The novelty of this research lies in the integration of these advanced technologies—Blockchain, SDN, Bi-GRU deep learning, and Digital Twin Technology—to improve the security, efficiency, and real-time monitoring of IoT-enabled smart grids. This study synthesizes these high-level technologies into a secure and highly efficient smart grid system, capable of adapting in real-time to prevent adverse events. Blockchain and SDN implementation enhances data integrity and network stability, while the Bi-GRU model offers superior accuracy in energy prediction. The integration of Digital Twin Technology enables real-time monitoring and supports decision-making processes, providing a robust solution for the future of smart grids.

关键词： Deep learning Time series analysis Predictive models Real-time systems Stability analysis Smart grids Digital twins Blockchains Security Software defined networking

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