Jeongeun Park,
Jeongeun Park
Institution: Department of Artificial Intelligence
Email: baro0906@korea.ac.kr
Seungyoun Shin,
Seungyoun Shin
Institution: Department Computer Engineering
Email: info@rnfinity.com
Sangheum Hwang
Sangheum Hwang
Institution: Department of Data Science, Seoul National University of Science and Technology
Email: info@rnfinity.com
Robust learning methods aim to learn a clean target distribution from noisy and corrupted training data where a specific corruption pattern is often assumed a priori. Our proposed method can not only successfully learn the clean target distribution from a dirty dataset but also can estimate the unde...
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Robust learning methods aim to learn a clean target distribution from noisy and corrupted training data where a specific corruption pattern is often assumed a priori. Our proposed method can not only successfully learn the clean target distribution from a dirty dataset but also can estimate the underlying noise pattern. To this end, we leverage a mixture-of-experts model that can distinguish two different types of predictive uncertainty, aleatoric and epistemic uncertainty. We show that the ability to estimate the uncertainty plays a significant role in elucidating the corruption patterns as these two objectives are tightly intertwined. We also present a novel validation scheme for evaluating the performance of the corruption pattern estimation. Our proposed method is extensively assessed in terms of both robustness and corruption pattern estimation in the computer vision domain. Code has been made publicly available at https://github.com/jeongeun980906/Uncertainty-Aware-Robust-Learning.
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Posted 10 months ago
Joni Virta
Joni Virta
Institution: Department of Mathematics and Statistics
Email: joni.virta@utu.fi
We develop a dimension reduction framework for data consisting of matrices of counts. Our model is based on the assumption of existence of a small amount of independent normal latent variables that drive the dependency structure of the observed data, and can be seen as the exact discrete analogue of...
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We develop a dimension reduction framework for data consisting of matrices of counts. Our model is based on the assumption of existence of a small amount of independent normal latent variables that drive the dependency structure of the observed data, and can be seen as the exact discrete analogue of a contaminated low-rank matrix normal model. We derive estimators for the model parameters and establish their limiting normality. An extension of a recent proposal from the literature is used to estimate the latent dimension of the model. The method is shown to outperform both its vectorization-based competitors and matrix methods assuming the continuity of the data distribution in analysing simulated data and real world abundance data.
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Posted 10 months ago
Iman Azimi,
Iman Azimi
Institution: Department of Computer Science
Email: info@rnfinity.com
Arman Anzanpour,
Arman Anzanpour
Institution: Department of Computing
Email: info@rnfinity.com
Amir M. Rahmani
Amir M. Rahmani
Institution: Department of Computer Science
Email: info@rnfinity.com
Photoplethysmography (PPG) is a non-invasive technique used in wearable devices to measure vital signs (e.g., heart rate). The method is, however, highly susceptible to motion artifacts, which are inevitable in remote health monitoring. Noise reduces signal quality, leading to inaccurate decision-ma...
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Photoplethysmography (PPG) is a non-invasive technique used in wearable devices to measure vital signs (e.g., heart rate). The method is, however, highly susceptible to motion artifacts, which are inevitable in remote health monitoring. Noise reduces signal quality, leading to inaccurate decision-making. In addition, unreliable data collection and transmission waste a massive amount of energy on battery-powered devices. Studies in the literature have proposed PPG signal quality assessment (SQA) enabled by rule-based and machine learning (ML)-based methods. However, rule-based techniques were designed according to certain specifications, resulting in lower accuracy with unseen noise and artifacts. ML methods have mainly been developed to ensure high accuracy without considering execution time and device’s energy consumption. In this paper, we propose a lightweight and energy-efficient PPG SQA method enabled by a semi-supervised learning strategy for edge devices. We first extract a wide range of features from PPG and then select the best features in terms of accuracy and latency. Second, we train a one-class support vector machine model to classify PPG signals into “Reliable” and “Unreliable” classes. We evaluate the proposed method in terms of accuracy, execution time, and energy consumption on two embedded devices, in comparison to five state-of-the-art PPG SQA methods. The methods are assessed using a PPG dataset collected via smartwatches from 46 individuals in free-living conditions. The proposed method outperforms the other methods by achieving an accuracy of 0.97 and a false positive rate of 0.01. It also provides the lowest latency and energy consumption compared to other ML-based methods.
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Posted 10 months ago
Sudarsana Reddy Kadiri,
Sudarsana Reddy Kadiri
Institution: Department of Information and Communications Engineering
Email: sudarsana.kadiri@aalto.fi
Paavo Alku
Paavo Alku
Institution: Department of Information and Communications Engineering
Email: info@rnfinity.com
In this study, formant tracking is investigated by refining the formants tracked by an existing data-driven tracker, DeepFormants, using the formants estimated in a model-driven manner by linear prediction (LP)-based methods. As LP-based formant estimation methods, conventional covariance analysis (...
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In this study, formant tracking is investigated by refining the formants tracked by an existing data-driven tracker, DeepFormants, using the formants estimated in a model-driven manner by linear prediction (LP)-based methods. As LP-based formant estimation methods, conventional covariance analysis (LP-COV) and the recently proposed quasi-closed phase forward–backward (QCP-FB) analysis are used. In the proposed refinement approach, the contours of the three lowest formants are first predicted by the data-driven DeepFormants tracker, and the predicted formants are replaced frame-wise with local spectral peaks shown by the model-driven LP-based methods. The refinement procedure can be plugged into the DeepFormants tracker with no need for any new data learning. Two refined DeepFormants trackers were compared with the original DeepFormants and with five known traditional trackers using the popular vocal tract resonance (VTR) corpus. The results indicated that the data-driven DeepFormants trackers outperformed the conventional trackers and that the best performance was obtained by refining the formants predicted by DeepFormants using QCP-FB analysis. In addition, by tracking formants using VTR speech that was corrupted by additive noise, the study showed that the refined DeepFormants trackers were more resilient to noise than the reference trackers. In general, these results suggest that LP-based model-driven approaches, which have traditionally been used in formant estimation, can be combined with a modern data-driven tracker easily with no further training to improve the tracker’s performance.
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Posted 10 months ago
There has been an increasing interest in the development of deep-learning models for the large data processing such as images, audio, or video. Image processing has made breakthroughs in addressing important problems such as genome-wide biological networks, map interactions of genes and proteins, ne...
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There has been an increasing interest in the development of deep-learning models for the large data processing such as images, audio, or video. Image processing has made breakthroughs in addressing important problems such as genome-wide biological networks, map interactions of genes and proteins, network, etc. With the increase in sophistication of the system, and other areas such as internet of things, social media, web development, etc., the need for classification of image data has been felt more than ever before. It is more important to develop intelligent approaches that can take care of the sophistication of systems. Several researchers are working on the real-time images to solve the problems related to the classification of images. The algorithms to be developed will have to meet the large image datasets. In this paper, the generalized hierarchical fuzzy deep learning approach is discussed and developed to meet such demands. The objective is to design the algorithm for image classification so that it results in high accuracy. The approach is for real-life intelligent systems and the classification results have been shared for large image datasets such as the YaleB database. The accuracy of the algorithm has been obtained for various classes of images using image thresholding. The development of learning algorithms has been validated on corrupted and noisy data and results of various classes of images are presented.
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Posted 10 months ago
Youchen Fan,
Youchen Fan
Institution: School of Space Information, Space Engineering University
Email: love193777@sina.com
Mingyu Qin,
Mingyu Qin
Institution: Graduate School, Space Engineering University
Email: info@rnfinity.com
Huichao Guo
Huichao Guo
Institution: Department of Electronic and Optical Engineering, Space Engineering University
Email: info@rnfinity.com
The range-gated laser imaging instrument can capture face images in a dark environment, which provides a new idea for long-distance face recognition at night. However, the laser image has low contrast, low SNR and no color information, which affects observation and recognition. Therefore, it becomes...
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The range-gated laser imaging instrument can capture face images in a dark environment, which provides a new idea for long-distance face recognition at night. However, the laser image has low contrast, low SNR and no color information, which affects observation and recognition. Therefore, it becomes important to convert laser images into visible images and then identify them. For image translation, we propose a laser-visible face image translation model combined with spectral normalization (SN-CycleGAN). We add spectral normalization layers to the discriminator to solve the problem of low image translation quality caused by the difficulty of training the generative adversarial network. The content reconstruction loss function based on the Y channel is added to reduce the error mapping. The face generated by the improved model on the self-built laser-visible face image dataset has better visual quality, which reduces the error mapping and basically retains the structural features of the target compared with other models. The FID value of evaluation index is 36.845, which is 16.902, 13.781, 10.056, 57.722, 62.598 and 0.761 lower than the CycleGAN, Pix2Pix, UNIT, UGATIT, StarGAN and DCLGAN models, respectively. For the face recognition of translated images, we propose a laser-visible face recognition model based on feature retention. The shallow feature maps with identity information are directly connected to the decoder to solve the problem of identity information loss in network transmission. The domain loss function based on triplet loss is added to constrain the style between domains. We use pre-trained FaceNet to recognize generated visible face images and obtain the recognition accuracy of Rank-1. The recognition accuracy of the images generated by the improved model reaches 76.9%, which is greatly improved compared with the above models and 19.2% higher than that of laser face recognition.
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Posted 10 months ago
Ilias Gialampoukidis,
Ilias Gialampoukidis
Institution: Information Technologies Institute, Centre for Research and Technology Hellas,
Email: heliasgj@iti.gr
Thomas Papadimos,
Thomas Papadimos
Institution: Information Technologies Institute, Centre for Research and Technology Hellas
Email: info@rnfinity.com
Stelios Andreadis,
Stelios Andreadis
Institution: Information Technologies Institute, Centre for Research and Technology Hellas
Email: info@rnfinity.com
Stefanos Vrochidis
Stefanos Vrochidis
Institution: Information Technologies Institute, Centre for Research and Technology Hellas
Email: info@rnfinity.com
This paper discusses the importance of detecting breaking events in real time to help emergency response workers, and how social media can be used to process large amounts of data quickly. Most event detection techniques have focused on either images or text, but combining the two can improve perfor...
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This paper discusses the importance of detecting breaking events in real time to help emergency response workers, and how social media can be used to process large amounts of data quickly. Most event detection techniques have focused on either images or text, but combining the two can improve performance. The authors present lessons learned from the Flood-related multimedia task in MediaEval2020, provide a dataset for reproducibility, and propose a new multimodal fusion method that uses Graph Neural Networks to combine image, text, and time information. Their method outperforms state-of-the-art approaches and can handle low-sample labelled data.
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Posted 10 months ago
Michael Krump
Michael Krump
Institution: Institute of Flight Systems, University of the Bundeswehr Munich, 85579 Neubiberg, Germany
Email: michael.krump@unibw.de
The performance of deep learning based algorithms is significantly influenced by the quantity and quality of the available training and test datasets. Since data acquisition is complex and expensive, especially in the field of airborne sensor data evaluation, the use of virtual simulation environmen...
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The performance of deep learning based algorithms is significantly influenced by the quantity and quality of the available training and test datasets. Since data acquisition is complex and expensive, especially in the field of airborne sensor data evaluation, the use of virtual simulation environments for generating synthetic data are increasingly sought. In this article, the complete process chain is evaluated regarding the use of synthetic data based on vehicle detection. Among other things, content-equivalent real and synthetic aerial images are used in the process. This includes, in the first step, the learning of models with different training data configurations and the evaluation of the resulting detection performance. Subsequently, a statistical evaluation procedure based on a classification chain with image descriptors as features is used to identify important influencing factors in this respect. The resulting findings are finally incorporated into the synthetic training data generation and in the last step, it is investigated to what extent an increase of the detection performance is possible. The overall objective of the experiments is to derive design guidelines for the generation and use of synthetic data.
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Posted 10 months ago
Nan Xia
Nan Xia
Institution: Faculty of Business and Law, Queensland University of Technology, Brisbane, Australia
Email: n.xia@hdr.qut.edu.au
This article is an examination of the extent to which traditional medical knowledge in China can be protected by intellectual property laws. The analysis begins by providing a global picture with regard to the historic origin of intellectual property, exploring the reasons why China does not have in...
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This article is an examination of the extent to which traditional medical knowledge in China can be protected by intellectual property laws. The analysis begins by providing a global picture with regard to the historic origin of intellectual property, exploring the reasons why China does not have indigenous counterparts to the western system of intellectual property rights protecting its traditional knowledge (including traditional medical knowledge) and stating the problems of transplanting western intellectual property standards in China. A discussion follows on how China, under foreign pressure, has made efforts to comply with the changing standards mandated by various international, regional, and bilateral arrangements related to intellectual property, with examples of the development of China's patent law. China's approach towards the protection of traditional medical knowledge in various international fora related to intellectual property is explored. Finally, there is a specific examination of the compatibilities between the western system of intellectual property rights and traditional medical knowledge in China, at the national and community levels. This article argues that the system of intellectual property rights does not easily fit with China's traditional medical knowledge because of China's unique cultural traits, distinctive historical context and wide ethnic, religious, and local community diversity.
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Posted 11 months ago
Robert Burns,
Robert Burns
Institution: School of Natural Resources, West Virginia University, Morgantown, WV 26506, USA
Email: robert.burns@mail.wvu.edu
Marieke Lemmen,
Marieke Lemmen
Institution: School of Natural Resources, West Virginia University, Morgantown, WV 26506, USA
Email: info@rnfinity.com
Ross G. Andrew,
Ross G. Andrew
Institution: School of Natural Resources, West Virginia University, Morgantown, WV 26506, USA
Email: info@rnfinity.com
Jasmine Cardozo Moreira
Jasmine Cardozo Moreira
Institution: Tourism Department, Universidade Estadual de Ponta Grossa, Ponta Grossa 84010-330, PR, Brazil
Email: info@rnfinity.com
Marine sanctuaries serve as popular destinations for ecotourism, natural resource exploration, and recreation across the US. While often positive, visitation in marine and coastal areas can cause ecological threats to these ecosystems. Increased visitation in marine environments has led to the need ...
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Marine sanctuaries serve as popular destinations for ecotourism, natural resource exploration, and recreation across the US. While often positive, visitation in marine and coastal areas can cause ecological threats to these ecosystems. Increased visitation in marine environments has led to the need for management due to negative ecological and social impacts. Understanding environmental values, attitudes, and perceptions is important to the success of environmental protection. Using online surveys sent via Qualtrics asking questions regarding the users’ knowledge, attitudes, and perceptions of ocean resources, goods and services, this research focused on identifying user profiles and understanding their environmental perception associated with Gray’s Reef National Marine Sanctuary, an offshore marine protected area, and surrounding coastal Georgia. The results show that across multiple types of threats or phenomena, respondents are most concerned about threats to resources related to pollution. Furthermore, they support marine protection and are willing to adjust their consumption habits, such as recycling and energy use, to ensure the sustainable use of ocean resources. The inclusion of insights achieved through research about visitor perceptions into management decision making and planning can positively contribute to the success of environmental protection.
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Posted 11 months ago