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10th International Conference on machine learning and Applications, ICMLA 2011

作者： Cevikalp, Hakan Electrical and Electronics Engineering Department Machine Learning and Computer Vision Laboratory Eskisehir Osmangazi University Meselik Eskisehir 26480 Turkey

ISBN: (纸本)9780769546070

This paper introduces a binary large margin classifier that approximates each class with an hyper disk constructed from its training samples. For any pair of classes approximated with hyper disks, there is a corresponding linear separating hyper plane that maximizes the margin between them, and this can be found by solving a convex program that finds the closest pair of points on the hyper disks. More precisely, the best separating hyper plane is chosen to be the one that is orthogonal to the line segment connecting the closest points on the hyper disks and at the same time bisects the line. The method is extended to the nonlinear case by using the kernel trick, and the multi-class classification problems are dealt with constructing and combining several binary classifiers as in Support Vector machine (SVM) classifier. The experiments on several databases show that the proposed method compares favorably to other popular large margin classifiers. © 2011 IEEE.

关键词： Support vector machines

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Underwater Object Detection Enhancement via Channel Stabilization

Underwater Object Detection Enhancement via Channel Stabiliz...

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2022 International Conference on Digital Image Computing: Techniques and Applications, DICTA 2022

作者： Ali, Muhammad Khan, Salman Department of Machine Learning Mohamed bin Zayed University of AI Abu Dhabi UAE Department of Computer Vision Mohamed bin Zayed University of AI Abu Dhabi UAE

ISBN: (纸本)9781665456425

The complex marine environment exacerbates the challenges of object Abstract-The complex marine environment exacerbates the challenges of object detection manifold. With the advent of the modern era, marine trash presents a danger to the aquatic ecosystem, and it has always been challenging to address this issue with complete grip. Therefore, there is a significant need to precisely detect marine deposits and locate them accurately in challenging aquatic surroundings. To ensure the safety of the marine environment caused by waste, the deployment of underwater object detection is a crucial tool to mitigate the harm of such waste. Our work explains the image enhancement strategies used and experiments exploring the best detection obtained after applying these methods. Specifically, we evaluate Detectron 2's backbones performance using different base models and configurations for the underwater detection task. We first propose a channel stabilization technique on top of a simplified image enhancement model to help reduce haze and colour cast in training images. The proposed procedure shows improved results on multi-scale size objects present in the data set. After processing the images, we explore various backbones in Detectron2 to give the best detection accuracy for these images. In addition, we use a sharpening filter with augmentation techniques. This highlights the profile of the object which helps us recognize it easily. We demonstrate our results by verifying these on TrashCan Data set, both instance and material version. We then explore the best-performing backbone method for this setting. We apply our channel stabilization and augmentation methods to the best-performing technique. We also compare our detection results from Detectron2 using the best backbones with those from Deformable Transformer. The detection result for small size objects in the Instance-version of TRASHCAN 1.0 gives us a 9.53box we get the absolute gain of 7to the baseline. © 2022 IEEE.

关键词： Image enhancement

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Wi-Fi Signal Analysis for Heartbeat and Metal Detection: A Comparative Study of Reliable Contactless Systems 6

Wi-Fi Signal Analysis for Heartbeat and Metal Detection: A C...

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6th International Conference on Dependable Systems and Their Applications, DSA 2019

作者： Khan, Faiza Alam Bhuiyan, Md Zakirul Islam, Md Monirul Wang, Tian Zaman, Aliuz Tao, Hai Fordham University Department of Computer and Information Sciences New York United States Huaqiao University College of Computer Science and Technology Key Laboratory of Computer Vision and Machine Learning Huaqiao361021 China School of Computer Science Baoji University of Arts and Sciences Baoji Shaanxi721007 China

ISBN: (纸本)9781728160573

In this survey paper, we present various proposals of Wi-Fi applications which can benefit society, namely through detection of vital signs and detection of suspicious objects. Based on the previous research conducted in this field, we analyze how heart rate detection using Wi-Fi dependent technologies compare to older, sensor-based methods and the necessity of modern implementations especially in the medical field. People can have heart problems such as tachycardia and bradycardia at any instance, thus systems which do not require contact are most practical in detection of vital sign irregularities. Sensorbased systems may depend on Wi-Fi technologies only for the communication between different components in the system, such as in the Message Queuing Telemetry Transport System. However, the presence of a sensor in these systems can be replaced by Wi-Fi technologies to detect the presence of people and monitor their corresponding heart rates in a contactless manner. Additionally, Wi-Fi dependent systems such as Vital Radio and Wi-Sleep can monitor the heart rates of multiple people at once. Therefore contactless, Wi-Fi based systems are most efficient and practical for daily use. Moreover, although contactless systems demonstrate the benefits of technological advancements, these systems can still be further improved since they tend to produce higher accuracy rates from shorter distances, as demonstrated by the UWB radar system and the Fresnel model. Contactless systems can be enhanced to detect multiple users at once at extended distances. After providing details of contact requiring and contactless systems for heart rate detection, we also provide other useful applications of Wi-Fi technology, such as in the field of metal detection, human fall detection, etc. © 2020 IEEE.

关键词： Heart

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Optimizing 7-DOF Robot Manipulator Path Using Deep Reinforcement learning Techniques

Optimizing 7-DOF Robot Manipulator Path Using Deep Reinforce...

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International Workshop on Robot Sensing (ROSE)

作者： Mariam Kashkash Abdulmotaleb El Saddik Mohsen Guizani Machine Learning Department MBZUAI UAE University of Ottawa Canada Computer Vision Department MBZUAI UAE

ISBN: (数字)9798350362367

ISBN: (纸本)9798350362374

This paper proposes three different Deep Rein-forcement learning (DRL) techniques to find a free-obstacle path for a 7-DOF Robot Manipulator (RM). The robot is the Kinova Jaco Assistive Robot arm; its DH parameters and kinematics are presented. The suggested DRL methods are Deep Q-Network (DQN), Actor-Critic (AC), and Proximal Policy Optimization (PPO) algorithms. The environment, state space, action space, and reward function are defined to suit all proposed methods. The experiment is run to validate the performance of these methods in finding the path for the RM in two different environments. These environments differ in obstacles and complexity. The reward and average reward evaluation were used to evaluate the proposed methods. The results showed that the three models found the path in the suggested environments. The comparison between these methods is presented and discussed in this paper. The result discussion clarified the superiority of the PPO method in finding the free-obstacle path in a complex environment.

关键词： Kinematics Manipulators Assistive robots Robot sensing systems Deep reinforcement learning Complexity theory Sensors

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Large Margin Classifier Based on Hyperdisks

Large Margin Classifier Based on Hyperdisks

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International Conference on machine learning and Applications (ICMLA)

作者： Hakan Cevikalp Electrical and Electronics Engineering Department Machine Learning and Computer Vision Laboratory Eskisehir Osmangazi University Eskisehir Turkey

关键词： Support vector machines Databases Kernel Training Approximation methods Optimization Accuracy

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Even big data is not enough: need for a novel reference modelling for forensic document authentication

Even big data is not enough: need for a novel reference mode...

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作者： Garain, Utpal Halder, Biswajit Computer Vision and Pattern Recognition Unit and Centre for Artificial Intelligence and Machine Learning Indian Statistical Institute 203 B. T. Road Kolkata700108 India Department of CSE Narula Institute of Technology Agarpara Kolkata700109 India

With the emergence of big data, deep learning (DL) approaches are becoming quite popular in many branches of science. Forensic science is no longer an exception. However, there are certain problems in forensic science where the solutions would hardly benefit from the recent advances in DL algorithms. Document authentication is one such problem where we can have many reference samples, and with the big data scenario probably we would have even more number of reference samples but number of defective or forged samples will remain an issue. Experts often encounter situations where there is no or hardly a scanty number of forged samples available. In such situation, employment of data-hungry algorithms would be inefficient as they will not be able to learn the forged samples properly. This paper addresses this problem and proposes a novel reference modelling framework for forensic document authentication. The approach is based on Mahalanobis space. Two questioned document examination problems have been studied to show the effectiveness of our reference modelling algorithm which has also been compared to a commonly used learning approach, namely neural network-based classification. © 2019, Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Big data

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Ischemic stroke identification based on EEG and EOG using ID convolutional neural network and batch normalization 8

Ischemic stroke identification based on EEG and EOG using ID...

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8th International Conference on Advanced computer Science and Information Systems, ICACSIS 2016

作者： Giri, Endang Purnama Fanany, Mohamad Ivan Arymurthy, Aniati Murni Wijaya, Sastra Kusuma Computer Sciences Department Faculty of Mathematics and Natural Sciences Bogor Agricultural University Bogor West Java16680 Indonesia Machine Learning and Computer Vision Laboratory Faculty of Computer Sciences Universitas Indonesia Depok West Java16424 Indonesia Department of Physics Faculty of Mathematics and Natural Sciences Universitas Indonesia Depok West Java16424 Indonesia

ISBN: (纸本)9781509046294

In 2015, stroke was the number one cause of death in Indonesia. The majority type of stroke is ischemic. The standard tool for diagnosing stroke is CT-Scan. For developing countries like Indonesia, the availability of CT-Scan is very limited and still relatively expensive. Because of the availability, another device that potential to diagnose stroke in Indonesia is EEG. Ischemic stroke occurs because of obstruction that can make the cerebral blood flow (CBF) on a person with stroke has become lower than CBF on a normal person (control) so that the EEG signal have a deceleration. On this study, we perform the ability of ID Convolutional Neural Network (1DCNN) to construct classification model that can distinguish the EEG and EOG stroke data from EEG and EOG control data. To accelerate training process our model we use Batch Normalization. Involving 62 person data object and from leave one out the scenario with five times repetition of measurement we obtain the average of accuracy 0.86 (F-Score 0.861) only at 200 epoch. This result is better than all over shallow and popular classifiers as the comparator (the best result of accuracy 0.69 and F-Score 0.72). The feature used in our study were only 24 handcrafted feature with simple feature extraction process. © 2016 IEEE.

关键词： computerized tomography

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Better Understanding Differences in Attribution Methods via Systematic Evaluations

arXiv

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arXiv 2023年

作者： Rao, Sukrut Böhle, Moritz Schiele, Bernt The Department of Computer Vision and Machine Learning Max Planck Institute for Informatics Saarland Informatics Campus Saarbrücken66123 Germany

Deep neural networks are very successful on many vision tasks, but hard to interpret due to their black box nature. To overcome this, various post-hoc attribution methods have been proposed to identify image regions most influential to the models' decisions. Evaluating such methods is challenging since no ground truth attributions exist. We thus propose three novel evaluation schemes to more reliably measure the faithfulness of those methods, to make comparisons between them more fair, and to make visual inspection more systematic. To address faithfulness, we propose a novel evaluation setting (DiFull) in which we carefully control which parts of the input can influence the output in order to distinguish possible from impossible attributions. To address fairness, we note that different methods are applied at different layers, which skews any comparison, and so evaluate all methods on the same layers (ML-Att) and discuss how this impacts their performance on quantitative metrics. For more systematic visualizations, we propose a scheme (AggAtt) to qualitatively evaluate the methods on complete datasets. We use these evaluation schemes to study strengths and shortcomings of some widely used attribution methods over a wide range of models. Finally, we propose a post-processing smoothing step that significantly improves the performance of some attribution methods, and discuss its applicability. Copyright © 2023, The Authors. All rights reserved.

关键词： Deep neural networks

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Pruning neural network models for gene regulatory dynamics using data and domain knowledge 38

Pruning neural network models for gene regulatory dynamics u...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Hossain, Intekhab Fischer, Jonas Burkholz, Rebekka Quackenbush, John Department of Biostatistics Harvard T.H. Chan School of Public Health BostonMA02115 United States Dep. for Computer Vision and Machine Learning Max Planck Institute for Informatics Saarbrücken Germany Helmholtz Center CISPA for Information Security Saarbrücken Germany

The practical utility of machine learning models in the sciences often hinges on their interpretability. It is common to assess a model's merit for scientific discovery, and thus novel insights, by how well it aligns with already available domain knowledge-a dimension that is currently largely disregarded in the comparison of neural network models. While pruning can simplify deep neural network architectures and excels in identifying sparse models, as we show in the context of gene regulatory network inference, state-of-the-art techniques struggle with biologically meaningful structure learning. To address this issue, we propose DASH‡, a generalizable framework that guides network pruning by using domain-specific structural information in model fitting and leads to sparser, better interpretable models that are more robust to noise. Using both synthetic data with ground truth information, as well as real-world gene expression data, we show that DASH, using knowledge about gene interaction partners within the putative regulatory network, outperforms general pruning methods by a large margin and yields deeper insights into the biological systems being studied. © 2024 Neural information processing systems foundation. All rights reserved.

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DWDN: Deep Wiener Deconvolution Network for Non-Blind Image Deblurring

arXiv

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arXiv 2021年

作者： Dong, Jiangxin Roth, Stefan Schiele, Bernt School of Computer Science and Engineering Nanjing University of Science and Technology China Department of Computer Vision and Machine Learning Max Planck Institute for Informatics Germany Department of Computer Science TU Darmstadt Germany

We present a simple and effective approach for non-blind image deblurring, combining classical techniques and deep learning. In contrast to existing methods that deblur the image directly in the standard image space, we propose to perform an explicit deconvolution process in a feature space by integrating a classical Wiener deconvolution framework with learned deep features. A multi-scale cascaded feature refinement module then predicts the deblurred image from the deconvolved deep features, progressively recovering detail and small-scale structures. The proposed model is trained in an end-to-end manner and evaluated on scenarios with simulated Gaussian noise, saturated pixels, or JPEG compression artifacts as well as real-world images. Moreover, we present detailed analyses of the benefit of the feature-based Wiener deconvolution and of the multi-scale cascaded feature refinement as well as the robustness of the proposed approach. Our extensive experimental results show that the proposed deep Wiener deconvolution network facilitates deblurred results with visibly fewer artifacts and quantitatively outperforms state-of-the-art non-blind image deblurring methods by a wide margin. Copyright © 2021, The Authors. All rights reserved.

关键词： Deconvolution

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