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2023 International Conference on Machine Learning and Cybernetics, ICMLC 2023

作者： Ali, Md. Anas Haque, Mahmudul Alam, Saadia Binte Rahman, Rashedur Amin, Mashraful Kobashi, Syoji Graduate School of Engineering University of Hyogo Himeji Hyogo Japan Independent University Department of Computer Science and Engineering Bangladesh Dhaka1299 Bangladesh Independent University Center for Computational & Data Sciences Bangladesh Dhaka1299 Bangladesh

ISBN: (纸本)9798350303780

In recent years, healthcare and safety have been a major focus of deep learning research. This paper focuses on the detection of Medical Personal Protective Equipment (MPPE) in the health-care sector using YOLOv7. Improper use of personal protective equipment (PPE) can result in the contamination and cross-contamination of infectious diseases, so it is crucial for healthcare professionals to use it correctly. The CPPE-5 dataset was used to train the model, which contains 1029 high-quality images divided into five categories: coveralls, face shield, gloves, masks, and goggles. The objective of this research is to create an accurate model for future applications and development using a suitable medical PPE dataset. The proposed model outperforms previous studies, with an optimal mAP of 90.93 %, indicating that it is a promising method for detecting MPPE in the healthcare sector. © 2023 IEEE.

关键词： Object detection

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Decision Systems Optimization for Cybersecurity Resilience 12th

Decision Systems Optimization for Cybersecurity Resilience

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12th International Conference on Frontiers of Intelligent Computing: Theory and Applications, FICTA 2024

作者： Ikhar, Sharayu Mondal, Dipannita Khubalkar, Deepti Yadav, Ranjeev Deshpande, Pallavi Patil, Sonali Vrushabhasagar Yashika Journal Publications Pvt. Ltd Maharashtra Wardha India Artificial Intelligence and Data Science Department Dr. Y. Patil College of Engineering and Innovation Telegaon410507 India Nagpur Campus Pune India Computer Science and Engineering Arya College of Engineering Jaipur India EnTC VIIT Pune India Nutan Maharashtra Institute of Engineering and Technology Maharashtra Pune India

ISBN: (纸本)9789819601424

The increasing danger in the online environment highlights the necessity for strong and durable cybersecurity solutions. This study aims to improve Intrusion Detection Systems (IDS) by introducing a new hybrid method that merges Variational Autoencoders (VAEs) and Ant Colony Optimization (ACO) to enhance anomaly detection. The main goal is to enhance decision-making systems in IDS to attain a high level of accuracy, with a proven success rate of 99.3%. The Variational Autoencoder (VAE) functions as a tool for extracting features and reducing dimensionality by capturing latent representations of typical network behavior. The model can detect anomalous activities more accurately by using VAE to identify subtle patterns and deviations in network traffic. The Ant Colony Optimization (ACO) algorithm is incorporated into the decision-making process of the IDS to complement the VAE. Ant Colony Optimization (ACO) is used to optimize the parameters of Intrusion Detection Systems (IDS) and adjust the decision boundaries to improve the detection performance. The combined interaction of VAE and ACO forms a robust framework that can accurately detect anomalies and adjust in real-time to changing cyber threats. The hybrid model proposed was assessed with real-world cybersecurity datasets, showing an impressive accuracy of 99.3%. The experimental results highlight the VAE-ACO hybrid’s effectiveness in achieving high detection accuracy while remaining efficient in terms of computational resources. Combining Variational Autoencoders with Ant Colony Optimization shows great potential for enhancing decision systems in Intrusion Detection Systems. The 99.3% accuracy demonstrates the capability of this hybrid approach to greatly enhance cybersecurity resilience against advanced and evolving cyber threats. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Ant colony optimization

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Principal Component Analysis in Space Forms

arXiv

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arXiv 2023年

作者： Tabaghi, Puoya Khanzadeh, Michael Wang, Yusu Mirarab, Siavash The Halıcıoglu Data Science Institute UCSD United States The Computer Science and Engineering Department UCSD United States The Electrical and Computer Engineering Department UCSD United States

Principal Component Analysis (PCA) is a workhorse of modern data science. While PCA assumes the data conforms to Euclidean geometry, for specific data types, such as hierarchical and cyclic data structures, other spaces are more appropriate. We study PCA in space forms;that is, those with constant curvatures. At a point on a Riemannian manifold, we can define a Riemannian affine subspace based on a set of tangent vectors. Finding the optimal low-dimensional affine subspace for given points in a space form amounts to dimensionality reduction. Our Space Form PCA (SFPCA) seeks the affine subspace that best represents a set of manifold-valued points with the minimum projection cost. We propose proper cost functions that enjoy two properties: (1) their optimal affine subspace is the solution to an eigenequation, and (2) optimal affine subspaces of different dimensions form a nested set. These properties provide advances over existing methods, which are mostly iterative algorithms with slow convergence and weaker theoretical guarantees. We evaluate the proposed SFPCA on real and simulated data in spherical and hyperbolic spaces. We show that it outperforms alternative methods in estimating true subspaces (in simulated data) with respect to convergence speed or accuracy, often both. © 2023, CC BY.

关键词： Principal component analysis

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Enhancing Residential Real Estate Search with Classification Strategies Using Diffusion and CLIP

Enhancing Residential Real Estate Search with Classification...

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IEEE International Conference on Consumer Electronics (ICCE)

作者： Haojin Deng Wandong Zhang Yimin Yang Eman Nejad Department of Electrical and Computer Engineering Western University London Canada Data Science Wahi Realty Inc. Toronto Canada

The significance of the real estate search engine in the economy necessitates the development of a reliable room image luxury level annotation method that addresses current limitations, including the inability to assess room quality, underutilization of deep network capacities, and the need for more annotated house images. This paper proposes a novel real estate image annotation model, leveraging the diffusion model and contrastive language-image pre-training (CLIP) network, through a multi-stage algorithm. First, the diffusion network is employed as a data augmentation technique to generate additional real estate images for network training. Then, a CLIP model is utilized to categorize images into the kitchen, bathroom, dining room, living room, and foyer. Finally, five CLIP models assess the condition of each room, categorizing it as contemporary and standard. Experimental results on a newly collected real estate image dataset demonstrate that the proposed approach surpasses existing house image classification algorithms.

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One less reason for filter-pruning: gaining free adversarial robustness with structured grouped kernel pruning 23

One less reason for filter-pruning: gaining free adversarial...

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Proceedings of the 37th International Conference on Neural Information Processing Systems

作者： Shaochen (Henry) Zhong Zaichuan You Jiamu Zhang Sebastian Zhao Zachary LeClaire Zirui Liu Daochen Zha Vipin Chaudhary Shuai Xu Xia Hu Department of Computer Science Rice University Department of Computer and Data Sciences Case Western Reserve University Electrical Engineering and Computer Sciences UC Berkeley

Densely structured pruning methods utilizing simple pruning heuristics can deliver immediate compression and acceleration benefits with acceptable benign performances. However, empirical findings indicate such naïvely pruned networks are extremely fragile under simple adversarial attacks. Naturally, we would be interested in knowing if such a phenomenon also holds for carefully designed modern structured pruning methods. If so, then to what extent is the severity? And what kind of remedies are available? Unfortunately, both questions remain largely unaddressed: no prior art is able to provide a thorough investigation on the adversarial performance of modern structured pruning methods (spoiler: it is not good), yet the few works that attempt to provide mitigation often do so at various extra costs with only to-be-desired *** this work, we answer both questions by fairly and comprehensively investigating the adversarial performance of 10+ popular structured pruning methods. Solution-wise, we take advantage of Grouped Kernel Pruning (GKP)'s recent success in pushing densely structured pruning freedom to a more fine-grained level. By mixing up kernel smoothness — a classic robustness-related kernel-level metric — into a modified GKP procedure, we present a one-shot-post-train-weight-dependent GKP method capable of advancing SOTA performance on both the benign and adversarial scale, while requiring no extra (in fact, often less) cost than a standard pruning procedure. Please refer to our GitHub repository for code implementation, tool sharing, and model checkpoints.

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A New Personalized Efficacy Atlas for Pallidal Deep Brain Stimulation

A New Personalized Efficacy Atlas for Pallidal Deep Brain St...

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

作者： Xiongbiao Luo Department of Computer Science Xiamen University Xiamen China National Institute for Data Science in Health and Medicine Xiamen University Xiamen China

Neurostimulation is to implant implants electrodes into deep brain structures to treat drug-resistant motor disorder in Parkinson’s disease. Unfortunately, it remains challenging to find the optimal electrode implanted and activated location at deep anatomical regions and achieve the optimal surgical function or performance on patients. This paper proposes to create a novel personalized efficacy atlas that warps functional scales and estimates activation volume by modeling electric field to characterize the link between electrode location and neurosurgical performance. We used a population of 32 globus pallidus stimulated patient data to construct such an atlas. The experimental results demonstrate that modeling electric field centered at actually electrode implanted positions outperforms Gaussian kernel modeling to predict activation volume for functional atlas creation. In particular, our new atlas provides an automatic and accurate electrode implantation method with a guidance accuracy 1.26 mm, which is better than other approaches. Additionally, two phases of the functional scales obtained from 3 and 6 months after neurostimulation were compared to create the new atlas. The 6-month phase gives a better efficacy map.

关键词： Electrodes Solid modeling Sociology Implants Predictive models Neurostimulation Signal processing

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Realizing the Potential of Metaverse to Revolutionize the Future of Healthcare

Realizing the Potential of Metaverse to Revolutionize the Fu...

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Cyber Resilience (ICCR), International Conference on

作者： Abdelraouf Ishtaiwi Wael Hadi Ahmad Al-Qerem Department of Data Science and Artificial Intelligence University of Petra Amman Jordan Department of Information Security University of Petra Amman Jordan Computer Science Department Faculty of Information Technology Zarqa University Zarqa Jordan

ISBN: (数字)9798350394962

ISBN: (纸本)9798350394979

The emergence of metaverse technology, underpinned by virtual reality, augmented reality, and artificial intelligence, holds profound implications for transforming healthcare. This paper reviews the current applications of metaverse in medical education and training, mental healthcare, remote patient monitoring, surgical procedures, and biomedical research. It analyzes the challenges around adopting this technology, spanning technical limitations, ethical dilemmas, threats to privacy, and regulatory gaps. Finally, it proposes strategies and future directions to harness the metaverse's potential to increase healthcare access, efficiency, personalization and democratization. This encompasses research on innovative use cases, developing ethical governance frameworks, and policy reforms to incentivize equitable metaverse adoption across the healthcare ecosystem. A nuanced, evidence-based approach can enable the metaverse to usher a new paradigm of patient-centric healthcare delivery.

关键词： Training Ethics Privacy Uncertainty Metaverse Reviews Surgery

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Explainable convolutional neural network model provides an alternative genome-wide association perspective on mutations in SARS-CoV-2

arXiv

引用

arXiv 2024年

作者： Hatami, Parisa Annan, Richard Miranda, Luis Urias Gorman, Jane Xie, Mengjun Qingge, Letu Qin, Hong Department of Computer Science and Engineering University of Tennessee at Chattanooga ChattanoogaTN United States Department of Computer Science North Carolina Agricultural and Technical State University GreensboroNC United States Department of Mathematics Shenandoah University VA United States Department of Biology Catholic University of America DC United States School of Data Science Department of Computer Science Old Dominion University VA United States

Identifying mutations of SARS-CoV-2 strains associated with their phenotypic changes is critical for pandemic prediction and prevention. We compared an explainable convolutional neural network (CNN) approach and the traditional genome-wide association study (GWAS) on the mutations associated with WHO labels of SARS-CoV-2, a proxy for virulence phenotypes. We trained a CNN classification model that can predict genomic sequences into Variants of Concern (VOCs) and then applied Shapley Additive explanations (SHAP) model to identify mutations that are important for the correct predictions. For comparison, we performed traditional GWAS to identify mutations associated with VOCs. Comparison of the two approaches shows that the explainable neural network approach can more effectively reveal known nucleotide substitutions associated with VOCs, such as those in the spike gene regions. Our results suggest that explainable neural networks for genomic sequences offer a promising alternative to the traditional genome wide analysis approaches. © 2024, CC BY-NC-ND.

关键词： Genome

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On the computational and statistical complexity of over-parameterized matrix sensing

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The Journal of Machine Learning Research 2024年第1期25卷 8006-8052页

作者： Jiacheng Zhuo Jeongyeol Kwon Nhat Ho Constantine Caramanis Department of Computer Science University of Texas Austin TX Wisconsin Institute for Discovery University of Wisconsin-Madison Madison WI Department of Statistics and Data Sciences University of Texas Austin TX Department of Electrical and Computer Engineering University of Texas Austin TX

We consider solving the low-rank matrix sensing problem with the Factorized Gradient Descent (FGD) method when the specified rank is larger than the true rank. We refer to this as over-parameterized matrix sensing. If the ground truth signal X* ∈ ℝd×d is of rank r, but we try to recover it using FF&Tau where F ∈ ℝd×k and k > r, the existing statistical analysis either no longer holds or produces a vacuous statistical error upper bound (infinity) due to the at local curvature of the loss function around the global maxima. By decomposing the factorized matrix F into separate column spaces to capture the impact of using k > r, we show that ||FtFt - X*||2F converges sub-linearly to a statistical error of Õ(kdσ2/n) after $\tilde{\mathcal{O}}(\frac{\sigma_{r}}{\sigma}\sqrt{\frac{n}{d}})$ iterations, where Ft is the output of FGD after t iterations, σ2 is the variance of the observation noise, σr is the r-th largest eigenvalue of X*, and n is the number of samples. With a precise characterization of the convergence behavior and the statistical error, our results, therefore, offer a comprehensive picture of the statistical and computational complexity if we solve the over-parameterized matrix sensing problem with FGD.

关键词： computational complexity statistical complexity over-parameterization matrix sensing matrix regression factorized gradient descent

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Asymptotics of Bayesian Uncertainty Estimation in Random Features Regression 37

Asymptotics of Bayesian Uncertainty Estimation in Random Fea...

引用

37th Conference on Neural Information Processing Systems, NeurIPS 2023

作者： Baek, Youngsoo Berchuck, Samuel I. Mukherjee, Sayan Department of Statistical Science Duke University DurhamNC27705 United States Department of Biostatistics & Bioinformatics Duke University DurhamNC27705 United States Center for Scalable Data Analysis and Artificial Intelligence Universität Leipzig Leipzig04105 Germany Deparments of Mathematics Computer Science Biostatistics & Bioinformatics Duke University NC United States

ISBN: (纸本)9781713899921

In this paper, we compare and contrast the behavior of the posterior predictive distribution to the risk of the maximum a posteriori (MAP) estimator for the random features regression model in the overparameterized regime. We will focus on the variance of the posterior predictive distribution (Bayesian model average) and compare its asymptotics to that of the risk of the MAP estimator. In the regime where the model dimensions grow faster than any constant multiple of the number of samples, asymptotic agreement between these two quantities is governed by the phase transition in the signal-to-noise ratio. They also asymptotically agree with each other when the number of samples grows faster than any constant multiple of model dimensions. Numerical simulations illustrate finer distributional properties of the two quantities for finite dimensions. We conjecture they have Gaussian fluctuations and exhibit similar properties as found by previous authors in a Gaussian sequence model, which is of independent theoretical interest. © 2023 Neural information processing systems foundation. All rights reserved.

关键词： Signal to noise ratio

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