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arXiv 2025年

作者： Wang, Changhu Zhang, Ziheng Li, Jingyi Jessica Department of Statistics and Data Science University of California Los Angeles United States Department of Biostatistics University of California Los Angeles United States

Balancing false discovery rate (FDR) and statistical power to ensure reliable discoveries is a key challenge in high-dimensional feature selection. Although several FDR control methods have been proposed, most involve perturbing the original data, either by concatenating knockoff variables or splitting the data into two halves, both of which can lead to a loss of power. In this paper, we introduce a novel approach called Synthetic Null Parallelism (SyNPar), which controls the FDR in high-dimensional feature selection while preserving the original data. SyNPar generates synthetic null data from a model fitted to the original data and modified to reflect the null hypothesis. It then applies the same estimation procedure in parallel to both the original and synthetic null data to estimate coefficients that indicate feature importance. By comparing the coefficients estimated from the null data with those from the original data, SyNPar effectively identifies false positives, functioning as a numerical analog of a likelihood ratio test. We provide theoretical guarantees for FDR control at any desired level while ensuring that the power approaches one with high probability asymptotically. SyNPar is straightforward to implement and can be applied to a wide range of statistical models, including high-dimensional linear regression, generalized linear models, Cox models, and Gaussian graphical models. Through extensive simulations and real data applications, we demonstrate that SyNPar outperforms state-of-the-art methods, including knockoffs and data-splitting methods, in terms of FDR control, power, and computational efficiency. © 2025, CC BY-NC-ND.

关键词： data assimilation

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Improved Memory Type Product Estimator for Population Mean in Stratified Random Sampling Under Linear Cost Function

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SN computer science 2023年第3期4卷 1-9页

作者： Yadav, Subhash Kumar Vishwakarma, Gajendra K. Varshney, Rahul Pal, Arun Department of Statistics School of Physical and Decision Sciences Babasaheb Bhimrao Ambedkar University Lucknow 226025 India Department of Mathematics and Computing Indian Institute of Technology Dhanbad Dhanbad 826004 India Institute of Actuarial Science and Data Analytics UCSI University Kuala Lumpur 56000 Malaysia

In this article, we consider the memory type product estimator to estimate the population mean of the study variable in stratified random sampling. The suggested estimators’ bias and mean square error (MSE) expressions for the first order of approximation are derived. The proposed estimator is compared with the competing estimators under consideration and the efficiencies’ conditions are obtained. Further, we obtain the optimum allocation of samples in different strata along with MSEs. To achieve the solution of the problem, we frame our problem as AINLPP and solve through genetic programming technique. To show the efficiency of the estimator, we conduct simulation study. An empirical analysis based on real-life data is also offered to support the findings of the simulation study. The most efficient estimator is recommended for different practical applications. The suggested estimator is proven to be the best among the competing estimators. © 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： AINLPP Genetic programming technique Linear cost function Product estimator Stratified random sampling

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Automated Plant Leaf Classification using Ensemble Transfer Learning in CNN model

Automated Plant Leaf Classification using Ensemble Transfer ...

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2022 IEEE North Karnataka Subsection Flagship International Conference, NKCon 2022

作者： Yuvalatha, S. Keerthika, J. Prabhavathy, S. Banupriya, M. Priyadharshini, R. Bannari Amman Institute of Technology Department of Computer Science and Business Systems Sathyamangalam India Sri Eshwar College of Engineering Department of Computer Science and Engineering Coimbatore India Christ the King Engineering College Department of Electronics and Communication Engineering Coimbatore India Hindustan Institute of Technology Department of Artificial Intelligence and Data Science Coimbatore India Sri Ranganathar Institute of Engineering Department of Computer Science and Engineeeirng Coimbatore India

ISBN: (纸本)9781665453424

In the agriculture production, one of the biggest challenges is detecting the plant leaf disease in their early stages which significantly affects the farmer's earnings. In this paper various transfer learning CNN model of mobile NetV2, ResNet, InceptionV3, VGG16, Densenet are used to categories the potato leaf disease into healthy, Early blight, Late blight. data fragmentation strategies are applied to expand the number of images for the collected dataset to train the model. The mobileNetV2 achieve the accuracy of 86.8%. In this work the ensemble transfer learning for CNN model is proposed to identify the leaf disease. For the classification, majority voting ensemble techniques are used and obtained the accuracy of 94.8%. © 2022 IEEE.

关键词： Learning systems

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No-Reference Segmentation Annotation Quality Assessment

No-Reference Segmentation Annotation Quality Assessment

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IEEE International Conference on Multimedia and Expo (ICME)

作者： Zheng Lin Zheng-Peng Duan Xuying Zhang Luojun Lin Department of Computer Science and Technology Tsinghua University Beijing China College of Computer Science Nankai University Tianjin China College of Computer and Data Science Fuzhou University Fuzhou China

ISBN: (数字)9798350390155

ISBN: (纸本)9798350390162

Image segmentation tasks aim to separate the image into masks that represent different objects or regions, where deep-learning-based methods have become mainstream. In the common practice, researchers utilize large-scale datasets including images along with their annotations to train their models, and evaluate the predictions with evaluation metrics. However, to our knowledge, no metrics have been proposed to assess the quality of the segmentation annotations, which will bring benefits to both the labeling and experimental process. In this paper, we fill this research gap and propose the first no-reference segmentation annotation quality assessment named SAQ. Based on our observation, we utilize the normal gradients of pixels on the annotation contours to represent the degree of fitting the real contours, which reflect the annotation accuracy. To alleviate the image differences, we adopt the gradient ranking score rather than directly using the gradient value. The multi-scale strategy is introduced to accommodate annotations of objects with different structures. Extensive experiments on datasets for various segmentation tasks have demonstrated the rationality of our proposed SAQ, and the assessment results of their annotation quality can serve as significant references for researchers.

关键词： Measurement Image segmentation Accuracy Annotations Filtering Fitting Predictive models

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In-context convergence of transformers 24

In-context convergence of transformers

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Proceedings of the 41st International Conference on Machine Learning

作者： Yu Huang Yuan Cheng Yingbin Liang Department of Statistics and Data Science Wharton School University of Pennsylvania Philadelphia PA National University of Singapore Singapore Department of Electrical and Computer Engineering The Ohio State University Columbus OH

Transformers have recently revolutionized many domains in modern machine learning and one salient discovery is their remarkable in-context learning capability, where models can solve an unseen task by utilizing task-specific prompts without further parameters fine-tuning. This also inspired recent theoretical studies aiming to understand the in-context learning mechanism of transformers, which however focused only on linear transformers. In this work, we take the first step toward studying the learning dynamics of a one-layer transformer with softmax attention trained via gradient descent in order to in-context learn linear function classes. We consider a structured data model, where each token is randomly sampled from a set of feature vectors in either balanced or imbalanced fashion. For data with balanced features, we establish the finite-time convergence guarantee with near-zero prediction error by navigating our analysis over two phases of the training dynamics of the attention map. More notably, for data with imbalanced features, we show that the learning dynamics take a stagewise convergence process, where the transformer first converges to a near-zero prediction error for the query tokens of dominant features, and then converges later to a near-zero error for query tokens of under-represented features, via one and four training phases. Our proof features new techniques for analyzing the competing strengths of two types of attention weights, the change of which determines different training phases.

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FOXANN: A Method for Boosting Neural Network Performance

arXiv

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arXiv 2024年

作者： Jumaah, Mahmood A. Ali, Yossra H. Rashid, Tarik A. Vimal, S. Department of Computer Science University of Technology- Iraq Baghdad Iraq Department of Computer Science and Engineering University of Kurdistan Hewlêr Erbil Iraq Department of Artificial Intelligence and Data Science Ramco Institute of Technology Rajapalayam India

Artificial neural networks play a crucial role in machine learning and there is a need to improve their performance. This paper presents FOXANN, a novel classification model that combines the recently developed Fox optimizer with ANN to solve ML problems. Fox optimizer replaces the backpropagation algorithm in ANN;optimizes synaptic weights;and achieves high classification accuracy with a minimum loss, improved model generalization, and interpretability. The performance of FOXANN is evaluated on three standard datasets: Iris Flower, Breast Cancer Wisconsin, and Wine. The results presented in this paper are derived from 100 epochs using 10-fold cross-validation, ensuring that all dataset samples are involved in both the training and validation stages. Moreover, the results show that FOXANN outperforms traditional ANN and logistic regression methods as well as other models proposed in the literature such as ABC-ANN, ABC-MNN, CROANN, and PSO-DNN, achieving a higher accuracy of 0.9969 and a lower validation loss of 0.0028. These results demonstrate that FOXANN is more effective than traditional methods and other proposed models across standard datasets. Thus, FOXANN effectively addresses the challenges in ML algorithms and improves classification performance. © 2024, CC BY-NC-ND.

关键词： Machine learning

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AIGC-Driven Real-Time Interactive 4D Traffic Scene Generation in Vehicular Networks

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IEEE Network 2025年

作者： Li, Xiaolong Deng, Ruiting Wei, Jianhao Wu, Xin Chen, Jiayuan Yi, Changyan Cai, Jun Niyato, Dusit Shen, Xuemin Hunan University of Technology and Business School of Computer Science China Nanjing University of Aeronautics and Astronautics College of Computer Science and Technology China Concordia University Department of Electrical and Computer Engineering Montreal Canada Nanyang Technological University College of Computing and Data Science Singapore University of Waterloo Department of Electrical and Computer Engineering Canada

Real-time, interactive 4D traffic scene generation enables rapid digital twinning of traffic scenarios, improving management and decision-making in intelligent transportation systems. However, current text-to-video models, such as Sora, struggle to maintain the temporal coherence of traffic elements and interact with dynamic environments and users when generating 4D scenes. This article introduces a novel cloud-edge-terminal collaborative framework that leverages Artificial Intelligence-Generated Content (AIGC) in vehicular networks to tackle these challenges, ensuring long-term coherence and improved interactivity. The framework presents a comprehensive architecture for real-time interactive 4D scene generation, encompassing data collection, management, model pre-training, fine-tuning, and inference. We examine key design requirements and challenges, demonstrating that our microservice-based framework enables the system to generate and update 4D traffic scenes in real time, effectively responding to traffic data and user inputs. To the best of our knowledge, this is the first successful implementation of real-time, interactive 4D traffic scene generation. Performance evaluations show the superiority of our framework, powered by microservice-based code fine-tuning, over traditional frameworks. Finally, we discuss future research directions to enhance AIGC-driven 4D traffic scene generation. © 1986-2012 IEEE.

关键词： Decision making

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Semënov Arithmetic, Affine VASS, and String Constraints

arXiv

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arXiv 2023年

作者： Draghici, Andrei Haase, Christoph Manea, Florin Department of Computer Science University of Oxford Oxford United Kingdom Computer Science Department and Campus-Institut Data Science Göttingen University Germany

We study extensions of Semënov arithmetic, the first-order theory of the structure x>. It is well-known that this theory becomes undecidable when extended with regular predicates over tuples of number strings, such as the Büchi V2-predicate. We therefore restrict ourselves to the existential theory of Semënov arithmetic and show that this theory is decidable in EXPSPACE when extended with arbitrary regular predicates over tuples of number strings. Our approach relies on a reduction to the language emptiness problem for a restricted class of affine vector addition systems with states, which we show decidable in EXPSPACE. As an application of our result, we settle an open problem from the literature and show decidability of a class of string constraints involving length constraints. © 2023, CC BY.

关键词： Computability and decidability

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A Review on Machine Learning and Deep Learning Methods to Fortify Cyberspace 1

A Review on Machine Learning and Deep Learning Methods to Fo...

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1st International Conference on Computational science and Technology, ICCST 2022

作者： Babu, N Junnu Bhargav, J. Mounica, Vanapalli Kumar, Eedupalli Sai Andhra Pradesh Madanapalle517325 India Department of Computer Science & Information Technology Lam 522034 India Department of Artificial Intelligence & Data Science Nambur 522508 India Department of Cse Ongole 523272 India

ISBN: (纸本)9781665476553

Things that have garnered and sparked a great deal of academic attention in the previous decade are Machine learning (ML) and Deep learning (DL). In most people's daily lives, online communities and social media have taken the lead recently, however, this trend comes with serious societal risks. Protecting sensitive data, data networks, and computers against malicious cyber attacks is a difficult task. Cyber security is essential for the safety of our data. Deep learning and machine learning, two relatively new technologies, are merged with cyber-attacks to give a solution to these issues. This article provides a summary of recent research into applying DL to the problem of cyber security, and it examines the many obstacles that must be overcome before this promising field can be fully implemented. © 2022 IEEE.

关键词： Cybersecurity

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An Intelligent Voice-Recognition Wheelchair System for Disabled Persons

An Intelligent Voice-Recognition Wheelchair System for Disab...

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2023 International Conference on Sustainable Computing and Smart Systems, ICSCSS 2023

作者： Kalpana, A.V. Venkataramanan, V. Charulatha, G. Geetha, G. Srm Institute of Science and Technology Department of Data Science and Business Systems Tamil Nadu Chennai India D.J Sanghvi College of Engineering Department of Electronics and Communication Engineering Maharashtra Mumbai India Peri Institute of Technology Department of Electronics and Communication Engineering Tamil Nadu Chennai India B v Raju Institute of Technology Department of Computer Science Engineering Telangana Hyderabad India

ISBN: (纸本)9798350333602

This study illustrates the creation of an intelligent voice-recognition wheelchair for disabled persons who cannot manually man-oeuvre their wheelchairs. Using voice recognition, the patient operates the wheelchair, and a GPS device included in the wheelchair can track the patient's position and communicate the data to a Smartphone. To carry out the people's orders, the voice module V3 records and can recognize the patient's voice. This module converts the voice commands into letters, subsequently transmitted to the Wi-Fi module to operate the wheelchair. The Wi-Fi module sends commands to the motor drive for the wheels. In addition, three levels of motor speed adjustment are available: low, medium, and high. This device also utilizes an Infrared sensor to automatically identify obstacles, allowing the patient's family members to know exactly where they are. Disabled People can be used this device, which simultaneously provides a voice-controlled wheelchair, obstacle detection, motor speed control, and patient GPS tracking via mobile phone;it will be a successful solution for disabled people across the world. © 2023 IEEE.

关键词： Wheelchairs

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