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3rd IEEE Global Conference for Advancement in Technology, GCAT 2022

作者： Sharma, Avinash Dankan Gowda, V. Deenadhayalan, R. Madasamy Raja, G. Anandaram, Harishchander Department of Computer Science Engineering Mullana Haryan Ambala India Bms Institute of Technology and Management Department of Electronics and Communication Engineering Karnataka Bangalore India Paavai Engineering College Department of It Tamilnadu Namakkal India Centre for Excellence in Computational Engineering and Networking Amrita Vishwa Vidyapeetham Tamilnadu Coimbatore India

ISBN: (数字)9781665468558

ISBN: (纸本)9781665468534

A trans-thoracic interpretation of the electrical activity of the heart throughout time collected and externally recorded by skin electrodes is electrocardiograph. An electrocardiographic device creates this recording, and it does not involve any invasive procedures. This research focuses on the extraction of fetal ECG from the maternal ECG using adaptive neuro fuzzy inference system (ANFIS) and undecimated wavelet transform (UWT), which combines the desirable qualities of ANFIS and UWT. The objective of this paper is to improve the accuracy of fetal ECG analysis. In a lot of applications, the network needs to imitate non-linear and time-varying functions, which means that the functions could change based on the incoming signal and other parameter alterations. This is necessary since non-linear functions are more complex than linear functions. In situations like these, a greater number of signals may be reproduced at random, and the approaches that are used were evaluated for their ability to accurately determine the fetal ECG as well as for their potential use in other diagnostic applications. This hybrid network was able to quickly tackle the difficult problem, making it superior to conventional adaptive approaches. © 2022 IEEE.

关键词： Electrocardiograms

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Comparative Analysis of Diverse Classification Algorithms of Machine Learning by Using Various Quality Metrics

Comparative Analysis of Diverse Classification Algorithms of...

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Cybernetics, Cognition and Machine Learning Applications (ICCCMLA), IEEE International Conference on

作者： Kathiresan V Dinesh G Velliangiri Sarveshwaran J.J. Jayakanth Kiruthika M Yuvaraj Remu Dept. of Computer Science and Engineering School of Technology (GST) GITAM University Bengaluru Karnataka India Department of Computational Intelligence SRM Institute of Science and Technology Kattankulathur Chen Department of Computational Intelligence SRM Institute of Science and Technology Kattankulathur chengalpattu

Machine learning classification algorithms and their applications are becoming increasingly popular in today's era of information science. Choosing an algorithm that is appropriate for the problem and application is a challenging task. The quality of the prediction model is significantly influenced by selecting the right algorithm. The purpose of the study and comparative analysis highlights the benefits and drawbacks of various classification algorithms from all perspectives. The Machine Learning (ML) based classification algorithms such as Logistic Regression (LR), K-Nearest Neighbour(KNN), DT (Decision Tree), SVM (Support Vector Machine), and Naïve Bayes (NB) are assessed for comparative analysis. These algorithms were tested and analysed using various datasets acquired and used from the UCIML repository. Algorithms are evaluated using well-established effective measures for accuracy, recall, and precision. A variety of datasets are used to monitor, measure, and compare each algorithm's performance in terms of metrics. Each algorithm's effectiveness and efficacy are analysed and compared from all aspects. Based on overall performance, the algorithms are ranked in the following order: logistic regression, decision tree, SVM, KNN, and Nave Bayes.

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Private algorithms for stochastic saddle points and variational inequalities: beyond euclidean geometry 24

Private algorithms for stochastic saddle points and variatio...

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Proceedings of the 38th 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 Inst. for Mathematical and Comput. Eng. Fac. de Matemáticas and Esc. de Ingeniería Pontificia Universidad Católica de Chile Department of Computer Science & Engineering The Ohio State University and Department of Computer Science University of Toronto Vector Institute

ISBN: (纸本)9798331314385

In this work, we conduct a systematic study of stochastic saddle point problems (SSP) and stochastic variational inequalities (SVI) under the constraint of (ε, δ)-differential privacy (DP) in both Euclidean and non-Euclidean setups. We first consider Lipschitz convex-concave SSPs in the ℓp/ℓq setup, p, q ∈ [1,2]. That is, we consider the case where the primal problem has an ℓp-setup (i.e., the primal parameter is constrained to an ℓp bounded domain and the loss is ℓp-Lipschitz with respect to the primal parameter) and the dual problem has an ℓq setup. Here, we obtain a bound of $\tilde{O}\big(\frac{1}{\sqrt{n}} + \frac{\sqrt{d}}{n\epsilon}\big)$ on the strong SP-gap, where n is the number of samples and d is the dimension. This rate is nearly optimal for any p, q ∈ [1,2]. Without additional assumptions, such as smoothness or linearity requirements, prior work under DP has only obtained this rate when p = q = 2 (i.e., only in the Euclidean setup). Further, existing algorithms have each only been shown to work for specific settings of p and q and under certain assumptions on the loss and the feasible set, whereas we provide a general algorithm for DP SSPs whenever p, q ∈ [1, 2]. Our result is obtained via a novel analysis of the recursive regularization algorithm. In particular, we develop new tools for analyzing generalization, which may be of independent interest. Next, we turn our attention towards SVIs with a monotone, bounded and Lipschitz operator and consider ℓp-setups, p ∈ [1, 2]. Here, we provide the first analysis which obtains a bound on the strong VI-gap of $\tilde{O}\big(\frac{1}{\sqrt{n}} + \frac{\sqrt{d}}{n\epsilon}\big)$. For p - 1 = Ω(1), this rate is near optimal due to existing lower bounds. To obtain this result, we develop a modified version of recursive regularization. Our analysis builds on the techniques we develop for SSPs as well as employing additional novel components which handle difficulties arising from adapting the recursive regularizat

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Advanced Zero-Shot Text-to-Speech for Background Removal and Preservation with Controllable Masked Speech Prediction

Advanced Zero-Shot Text-to-Speech for Background Removal and...

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

作者： Leying Zhang Wangyou Zhang Zhengyang Chen Yanmin Qian AI Institute Department of Computer Science and Engineering Auditory Cognition and Computational Acoustics Lab MoE Key Lab of Artificial Intelligence Shanghai Jiao Tong University Shanghai China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

The acoustic background plays a crucial role in natural conversation. It provides context and helps listeners understand the environment, but a strong background makes it difficult for listeners to understand spoken words. The appropriate handling of these backgrounds is situation-dependent: Although it may be necessary to remove background to ensure speech clarity, preserving the background is sometimes crucial to maintaining the contextual integrity of the speech. Despite recent advancements in zero-shot Text-to-Speech technologies, current systems often struggle with speech prompts containing backgrounds. To address these challenges, we propose a Controllable Masked Speech Prediction strategy coupled with a dual-speaker encoder, utilizing a task-related control signal to guide the prediction of dual background removal and preservation targets. Experimental results demonstrate that our approach enables precise control over the removal or preservation of background across various acoustic conditions and exhibits strong generalization capabilities in unseen scenarios.

关键词： Oral communication Predictive models Signal processing Acoustics Text to speech Speech processing

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Advancing Non-intrusive Suppression on Enhancement Distortion for Noise Robust ASR

Advancing Non-intrusive Suppression on Enhancement Distortio...

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

作者： Wei Wang Siyi Zhao Yanmin Qian AI Institute Department of Computer Science and Engineering Auditory Cognition and Computational Acoustics Lab MoE Key Lab of Artificial Intelligence Shanghai Jiao Tong University Shanghai China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Recent advancements in speech enhancement (SE) techniques have greatly improved speech clarity and intelligibility in challenging acoustic environments. However, integrating SE into automatic speech recognition (ASR) systems often results in performance degradation due to artifacts introduced during the enhancement process. While various methods have enhanced recognition accuracy in SE-ASR systems, they often require fine-tuning or re-training of SE or ASR models, which is impractical in many real-world applications. In this paper, we propose a lightweight distortion suppression (DS) network that addresses these artifacts without modifying the SE or ASR models, treating them as fixed black boxes. The DS module operates on the time-frequency (T-F) bands of the original and enhanced complex spectrograms, efficiently compensating for SE distortions using the original T-F information. We validate our approach through experiments on both Mandarin and English ASR tasks using monaural and multi-channel SE frontends, across various ASR backends. Results show that the DS module significantly improves the performance of SE-ASR systems, even when used with robust commercial ASR backends.

关键词： Degradation Time-frequency analysis Acoustic distortion Closed box Speech enhancement Acoustics Noise robustness Noise measurement Spectrogram Automatic speech recognition

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Thunderstorm Nowcasting with Recurrent-Convolution Deep Learning: A Case Study in Ho Chi Minh City 18

Thunderstorm Nowcasting with Recurrent-Convolution Deep Lear...

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18th International Conference on Advanced Computing and Analytics, ACOMPA 2024

作者： Khang, Ta Gia Van Gia Thinh, Nguyen Van Hoai, Tran An, Phan Thanh Huu, Pham Thanh An, Nguyen Minh Bao Ho Chi Minh City University of Technology IVS Joint Stock Company Falculty of Computer Science and Engineering Ho Chi Minh City Viet Nam Ho Chi Minh City University of Technology Institute of Mathematical and Computational Sciences Ho Chi Minh City Viet Nam Ho Chi Minh City University of Technology Faculty of Computer Science and Engineering Ho Chi Minh City Viet Nam Ho Chi Minh City University of Technology IVS Joint Stock Company Falculty of Applied Science Ho Chi Minh City ADAI LAB Nha Trang City Viet Nam

ISBN: (纸本)9798331542467

Machine learning for precipitation nowcasting using radar reflectivity images is a promising approach in weather forecasting. This paper proposes a new pipeline of using Convolutional Neural Networks (CNNs) integrated with Gated Recurrent Units (LSTM) and visual Attention mechanisms for quantitative precipitation nowcasting (QPN) using radar reflectivity images. We propose an end-to-end nowcasting network that leverages a Convolutional-LSTM with Axial Attention as the backbone to develop an adaptive forecasting system. Our results demonstrate the potential of deep learning techniques to significantly improve short-term weather predictions. The models were trained and validated using real-world Doppler radar data from Nha Be, Ho Chi Minh City, ensuring both practical relevance and applicability. Experimental results highlight that the combination of 3D Convolution, Convolutional-LSTM, and Axial Attention achieves superior performance, underscoring the effectiveness of this approach. © 2024 IEEE.

关键词： Weather forecasting

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dVRK-Si: The Next Generation da Vinci Research Kit

dVRK-Si: The Next Generation da Vinci Research Kit

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International Symposium on Medical Robotics (ISMR)

作者： Keshuai Xu Jie Ying Wu Anton Deguet Peter Kazanzides Department of Computer Science Johns Hopkins University Baltimore MD USA Laboratory for Computational Sensing and Robotics (LCSR) Johns Hopkins University Baltimore MD USA Department of Computer Science Vanderbilt Institute for Surgery and Engineering Vanderbilt University Nashville TN USA

ISBN: (数字)9798331599003

ISBN: (纸本)9798331599010

The da Vinci Research Kit (dVRK) is an opensource and open-hardware surgical robotics research platform that provides complete access to all levels of control on decommissioned da Vinci Surgical Systems. The original dVRK, released to the research community in 2012 and now installed at more than 40 institutions worldwide, is based on the first-generation da Vinci system released in 2000. In this work, we present dVRK-Si, an update that extends support to decommissioned second/third generation da Vinci S/Si patientside robots. We describe extensions to the system architecture that maintain compatibility and interoperability with the original dVRK system, thereby enabling a heterogeneous mix of dVRK and dVRK-Si arms in the same system. Additionally, we describe the following components specific to the dVRK-Si: (1) a new controller that features 10 channels of PWM motor drivers with digital current loops; (2) alternative (closed-source) firmwares for embedded devices in the robot arm that stream serialized sensor data using an open protocol; and (3) electronics to support the passive Setup Joints included in full systems. All electronics designs, software, and firmware (except as noted above) are provided open source to the community.

关键词： Medical robotics Systems architecture Arms Pulse width modulation Robot sensing systems Motors Manipulators Software Interoperability Microprogramming

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Splitting Guarantees for Prophet Inequalities via Nonlinear Systems

arXiv

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arXiv 2024年

作者： Brustle, Johannes Perez-Salazar, Sebastian Verdugo, Victor Department of Computer and Systems Sciences Sapienza University of Rome Italy Department of Computational Applied Mathematics and Operations Research Rice University United States Institute for Mathematical and Computational Engineering Pontificia Universidad Católica de Chile Chile Department of Industrial and Systems Engineering Pontificia Universidad Católica de Chile Chile

The prophet inequality is one of the cornerstone problems in optimal stopping theory and has become a crucial tool for designing sequential algorithms in Bayesian settings. In the i.i.d. k-selection prophet inequality problem, we sequentially observe n non-negative random values sampled from a known distribution. Each time, a decision is made to accept or reject the value, and under the constraint of accepting at most k. For k = 1, Hill and Kertz [Ann. Probab. 1982] provided an upper bound on the worst-case approximation ratio that was later matched by an algorithm of Correa et al. [Math. Oper. Res. 2021]. The worst-case tight approximation ratio for k = 1 is computed by studying a differential equation that naturally appears when analyzing the optimal dynamic programming policy. A similar result for k > 1 has remained elusive. In this work, we introduce a nonlinear system of differential equations for the i.i.d. k-selection prophet inequality that generalizes Hill and Kertz's equation when k = 1. Our nonlinear system is defined by k constants that determine its functional structure, and their summation provides a lower bound on the optimal policy's asymptotic approximation ratio for the i.i.d. k-selection prophet inequality. To obtain this result, we introduce for every k an infinite-dimensional linear programming formulation that fully characterizes the worst-case tight approximation ratio of the k-selection prophet inequality problem for every n, and then we follow a dual-fitting approach to link with our nonlinear system for sufficiently large values of n. As a corollary, we use our provable lower bounds to establish a tight approximation ratio for the stochastic sequential assignment problem in the i.i.d. non-negative regime. Copyright © 2024, The Authors. All rights reserved.

关键词： Nonlinear systems

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Distribution-free Conformal Prediction for Ordinal Classification

arXiv

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arXiv 2024年

作者： Chakraborty, Subhrasish Tyagi, Chhavi Qiao, Haiyan Guo, Wenge Department of Mathematical Sciences New Jersey Institute of Technology United States School of Computer Science & Engineering California State University San Bernardino United States

Conformal prediction is a general distribution-free approach for constructing prediction sets combined with any machine learning algorithm that achieve valid marginal or conditional coverage in finite samples. Ordinal classification is common in real applications where the target variable has natural ordering among the class labels. In this paper, we discuss constructing distribution-free prediction sets for such ordinal classification problems by leveraging the ideas of conformal prediction and multiple testing with FWER control. Newer conformal prediction methods are developed for constructing contiguous and non-contiguous prediction sets based on marginal and conditional (class-specific) conformal p-values, respectively. Theoretically, we prove that the proposed methods respectively achieve satisfactory levels of marginal and class-specific conditional coverages. Through simulation study and real data analysis, these proposed methods show promising performance compared to the existing conformal method. © 2024, CC BY.

关键词： Prediction models

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Reasonable Bounds for Combinatorial Lines of Length Three

arXiv

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arXiv 2024年

作者： Bhangale, Amey Khot, Subhash Liu, Yang P. Minzer, Dor Department of Computer Science and Engineering University of California Riverside United States Department of Computer Science Courant Institute of Mathematical Sciences New York University United States School of Mathematics Institute for Advanced Study PrincetonNJ United States Department of Mathematics Massachusetts Institute of Technology United States

We prove that any subset A ⊆ [3]n with 3−n|A| > (log log log log n)−c contains a combinatorial line of length 3, i.e., x, y, z ∈ A, not all equal, with xi = yi = zi or (xi, yi, zi) = (0, 1, 2) for all i = 1, 2, . . ., n. This improves on the previous best bound of 3−n|A| > Ω((log∗ n)−1/2) of [D.H.J. Polymath, Ann. of Math. 2012]. Copyright © 2024, The Authors. All rights reserved.

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