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International Conference on Dependable Systems and Their Applications (DSA)

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

ISBN: (数字)9781728160573

ISBN: (纸本)9781728160580

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. Sensor-based 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.

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Txt2Img-MHN: Remote Sensing Image Generation from Text Using Modern Hopfield Networks

arXiv

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arXiv 2022年

作者： Xu, Yonghao Yu, Weikang Ghamisi, Pedram Kopp, Michael Hochreiter, Sepp Vienna1030 Austria Computer Vision Laboratory Department of Electrical Engineering Linköping University Linköping58183 Sweden Helmholtz-Zentrum Dresden-Rossendorf Helmholtz Institute Freiberg for Resource Technology Machine Learning Group Freiberg09599 Germany ELLIS Unit Linz and LIT AI Lab Institute for Machine Learning Johannes Kepler University Linz4040 Austria

The synthesis of high-resolution remote sensing images based on text descriptions has great potential in many practical application scenarios. Although deep neural networks have achieved great success in many important remote sensing tasks, generating realistic remote sensing images from text descriptions is still very difficult. To address this challenge, we propose a novel text-to-image modern Hopfield network (Txt2Img-MHN). The main idea of Txt2Img-MHN is to conduct hierarchical prototype learning on both text and image embeddings with modern Hopfield layers. Instead of directly learning concrete but highly diverse text-image joint feature representations for different semantics, Txt2Img-MHN aims to learn the most representative prototypes from text-image embeddings, achieving a coarse-to-fine learning strategy. These learned prototypes can then be utilized to represent more complex semantics in the text-to-image generation task. To better evaluate the realism and semantic consistency of the generated images, we further conduct zero-shot classification on real remote sensing data using the classification model trained on synthesized images. Despite its simplicity, we find that the overall accuracy in the zero-shot classification may serve as a good metric to evaluate the ability to generate an image from text. Extensive experiments on the benchmark remote sensing text-image dataset demonstrate that the proposed Txt2Img-MHN can generate more realistic remote sensing images than existing methods. Code and pre-trained models are available online (https://***/YonghaoXu/Txt2Img-MHN). Copyright © 2022, The Authors. All rights reserved.

关键词： Remote sensing

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Looking Beyond Two Frames: End-to-End Multi-Object Tracking Using Spatial and Temporal Transformers

arXiv

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arXiv 2021年

作者： Zhu, Tianyu Hiller, Markus Ehsanpour, Mahsa Ma, Rongkai Drummond, Tom Reid, Ian Rezatofighi, Hamid The Department of Electrical and Computer Systems Engineering Monash University Australia The School of Computing and Information Systems The University of Melbourne Australia The Australian Institute for Machine Learning The University of Adelaide Australia The Department of Data Science and AI Monash University Australia The Australian Centre for Robotic Vision Australia

Tracking a time-varying indefinite number of objects in a video sequence over time remains a challenge despite recent advances in the field. Most existing approaches are not able to properly handle multi-object tracking challenges such as occlusion, in part because they ignore long-term temporal information. To address these shortcomings, we present MO3TR: a truly end-to-end Transformer-based online multi-object tracking (MOT) framework that learns to handle occlusions, track initiation and termination without the need for an explicit data association module or any heuristics. MO3TR encodes object interactions into long-term temporal embeddings using a combination of spatial and temporal Transformers, and recursively uses the information jointly with the input data to estimate the states of all tracked objects over time. The spatial attention mechanism enables our framework to learn implicit representations between all the objects and the objects to the measurements, while the temporal attention mechanism focuses on specific parts of past information, allowing our approach to resolve occlusions over multiple frames. Our experiments demonstrate the potential of this new approach, achieving results on par with or better than the current state-of-the-art on multiple MOT metrics for several popular multi-object tracking benchmarks. © 2021, CC BY.

关键词： Tracking (position)

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Support-Query Prototype Fusion Network for Few-shot Medical Image Segmentation

arXiv

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arXiv 2024年

作者： Wu, Xiaoxiao Gao, Zhenguo Chen, Xiaowei Wang, Yakai Qu, Shulei Li, Na Department of Computer Science and Technology Huaqiao University No.668 Jimei Avenue Fujian Xiamen361021 China Department of Mechanical Engineering and Automation Huaqiao University No.668 Jimei Avenue Fujian Xiamen361021 China Key Laboratory of Computer Vision and Machine Learning Fujian Provincial Universities Fujian Xiamen361021 China

In recent years, deep learning based on Convolutional Neural Networks (CNNs) has achieved remarkable success in many applications. However, their heavy reliance on extensive labeled data and limited generalization ability to unseen classes pose challenges to their suitability for medical image processing tasks. Few-shot learning, which utilizes a small amount of labeled data to generalize to unseen classes, has emerged as a critical research area, attracting substantial attention. Currently, most studies employ a prototype-based approach, in which prototypical networks are used to construct prototypes from the support set, guiding the processing of the query set to obtain the final results. While effective, this approach heavily relies on the support set while neglecting the query set, resulting in notable disparities within the model classes. To mitigate this drawback, we propose a novel Support-Query Prototype Fusion Network (SQPFNet). SQPFNet initially generates several support prototypes for the foreground areas of the support images, thus producing a coarse segmentation mask. Subsequently, a query prototype is constructed based on the coarse segmentation mask, additionally exploiting pattern information in the query set. Thus, SQPFNet constructs highquality support-query fused prototypes, upon which the query image is segmented to obtain the final refined query mask. Evaluation results on two public datasets, SABS and CMR, show that SQPFNet achieves state-of-the-art performance. © 2024, CC BY.

关键词： Image segmentation

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New Parameters to Monitor Rehabilitation after Tibial Fractures Based on Ground-Reaction Forces Enable Assessment of Heavily Altered Gait

SSRN

引用

SSRN 2024年

作者： Wolff, Christian Warmerdam, Elke Dahmen, Tim Pohlemann, Tim Slusallek, Philipp Ganse, Bergita Saarbrücken Germany Saarbrücken Graduate School of Computer Science Germany Departments and Institutes of Surgery Saarland University Homburg Germany Chair for Computer Vision and Machine Learning Hochschule Aalen Aalen Germany Department of Trauma Hand and Reconstructive Surgery Departments and Institutes of Surgery Saarland University Homburg Germany

Background and Objective: Instrumented insoles have created new opportunities for patient monitoring. As the shape of the ground-reaction force curve during the stance phase is altered in patients after lower-extremity fracture, classical parameters derived from the curve often cannot be used to monitor the early rehabilitation process. We proposed new parameters applicable to individuals in the early stages of recovery after a tibial fracture and hypothesized interrelation with healing progress. Methods: In an observational prospective longitudinal study, data were collected from 13 patients with tibial fractures during straight walking at each hospital visit using instrumented insoles with 16 pressure sensors at 100 Hz. To analyse changes of the new parameters throughout healing, parametrized curves were fitted. If successful, linear, polynomial (2nd and 3rd degree), and logarithmic regression analyses were conducted to determine the best fit reflected in the highest R2-value and lowest-fitting error. A Wald Test with t-distribution was employed for statistical analysis. Strides were classified as regular or non-regular, and changes in this proportion throughout healing were analysed. Results: The parameters with the highest R2-values were the mean force between enclosing turning points (R2 = 0.715), the absolute time between enclosing turning points (R2 = 0.707), and the highest overall force (R2 = 0.722). There was a significant increase of regular strides on both the injured (R2 = 0.427, p 2 = 0.506, p © 2024, The Authors. All rights reserved.

关键词： Patient monitoring

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Degrees-of-freedom penalized piecewise regression

arXiv

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arXiv 2023年

作者： Volz, Stefan Storath, Martin Weinmann, Andreas Lab for Mathematical Methods in Computer Vision and Machine Learning Technische Hochschule Würzburg-Schweinfurt Ignaz-Schön-Str. 11 Schweinfurt97421 Germany Department of Mathematics and Natural Sciences Hochschule Darmstadt Schöfferstraße 3 Darmstadt64295 Germany

Many popular piecewise regression models rely on minimizing a cost function on the model fit with a linear penalty on the number of segments. However, this penalty does not take into account varying complexities of the model functions on the segments potentially leading to overfitting when models with varying complexities, such as polynomials of different degrees, are used. In this work, we enhance on this approach by instead using a penalty on the sum of the degrees of freedom over all segments, called degrees-of-freedom penalized piecewise regression (DofPPR). We show that the solutions of the resulting minimization problem are unique for almost all input data in a least squares setting. We develop a fast algorithm which does not only compute a minimizer but also determines an optimal hyperparameter – in the sense of rolling cross validation with the one standard error rule – exactly. This eliminates manual hyperparameter selection. Our method supports optional user parameters for incorporating domain knowledge. We provide an open-source Python/Rust code for the piecewise polynomial least squares case which can be extended to further models. We demonstrate the practical utility through a simulation study and by applications to real data. A constrained variant of the proposed method gives state-of-the-art results in the Turing benchmark for unsupervised changepoint *** Codes 65K05 (Primary) 90C26 (Secondary) 62G05 Copyright © 2023, The Authors. All rights reserved.

关键词： Degrees of freedom (mechanics)

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

arXiv

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arXiv 2024年

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, CC BY-NC-SA.

关键词： Neural network models

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A Review of Generalized Zero-Shot learning Methods

arXiv

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arXiv 2020年

作者： Pourpanah, Farhad Abdar, Moloud Luo, Yuxuan Zhou, Xinlei Wang, Ran Lim, Chee Peng Wang, Xi-Zhao Jonathan Wu, Q.M. The Centre for Computer Vision and Deep Learning Department of Electrical and Computer Engineering University of Windsor WindsorONN9B 3P4 Canada Deakin University Australia The Department of Computer Science City University of Hong Kong Hong Kong The College of Mathematics and Statistics Shenzhen Key Lab. of Advanced Machine Learning and Applications Shenzhen University Shenzhen518060 China The College of Computer Science and Software Engineering Guangdong Key Lab. of Intelligent Information Processing Shenzhen University Shenzhen518060 China

Generalized zero-shot learning (GZSL) aims to train a model for classifying data samples under the condition that some output classes are unknown during supervised learning. To address this challenging task, GZSL leverages semantic information of the seen (source) and unseen (target) classes to bridge the gap between both seen and unseen classes. Since its introduction, many GZSL models have been formulated. In this review paper, we present a comprehensive review on GZSL. Firstly, we provide an overview of GZSL including the problems and challenges. Then, we introduce a hierarchical categorization for the GZSL methods and discuss the representative methods in each category. In addition, we discuss the available benchmark data sets and applications of GZSL, along with a discussion on the research gaps and directions for future investigations. Copyright © 2020, The Authors. All rights reserved.

关键词： Zero-shot learning

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Deep Interactive Segmentation of Medical Images: A Systematic Review and Taxonomy

arXiv

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arXiv 2023年

作者： Marinov, Zdravko Jäger, Paul F. Egger, Jan Kleesiek, Jens Stiefelhagen, Rainer The Computer Vision for Human-Computer Interaction Lab Department of Informatics Karlsruhe Institute of Technology Adenauerring 10 Karlsruhe76131 Germany Girardetstraße 2 Essen45131 Germany Heidelberg Interactive Machine Learning Group Im Neuenheimer Feld 223 Heidelberg69120 Germany The Helmholtz Imaging DKFZ Im Neuenheimer Feld 223 Heidelberg69120 Germany

Interactive segmentation is a crucial research area in medical image analysis aiming to boost the efficiency of costly annotations by incorporating human feedback. This feedback takes the form of clicks, scribbles, or masks and allows for iterative refinement of the model output so as to efficiently guide the system towards the desired behavior. In recent years, deep learning-based approaches have propelled results to a new level causing a rapid growth in the field with 121 methods proposed in the medical imaging domain alone. In this review, we provide a structured overview of this emerging field featuring a comprehensive taxonomy, a systematic review of existing methods, and an in-depth analysis of current practices. Based on these contributions, we discuss the challenges and opportunities in the field. For instance, we find that there is a severe lack of comparison across methods which needs to be tackled by standardized baselines and benchmarks. Appendixes are here. © 2023, CC BY.

关键词： Medical imaging

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HQG-Net: Unpaired Medical Image Enhancement with High-Quality Guidance

arXiv

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arXiv 2023年

作者： He, Chunming Li, Kai Xu, Guoxia Yan, Jiangpeng Tang, Longxiang Zhang, Yulun Li, Xiu Wang, Yaowei Tsinghua Shenzhen International Graduate School Tsinghua University Shenzhen518055 China Machine Learning Department NEC Laboratories America Inc. NJ08540 United States Department of Computer Science Norwegian University of Science and Technology Gjovik2815 Norway The Computer Vision Lab ETH Zürich Zürich8092 Switzerland Peng Cheng Laboratory Shenzhen518066 China

Unpaired Medical Image Enhancement (UMIE) aims to transform a low-quality (LQ) medical image into a high-quality (HQ) one without relying on paired images for training. While most existing approaches are based on Pix2Pix/CycleGAN and are effective to some extent, they fail to explicitly use HQ information to guide the enhancement process, which can lead to undesired artifacts and structural distortions. In this paper, we propose a novel UMIE approach that avoids the above limitation of existing methods by directly encoding HQ cues into the LQ enhancement process in a variational fashion and thus model the UMIE task under the joint distribution between the LQ and HQ domains. Specifically, we extract features from an HQ image and explicitly insert the features, which are expected to encode HQ cues, into the enhancement network to guide the LQ enhancement with the variational normalization module. We train the enhancement network adversarially with a discriminator to ensure the generated HQ image falls into the HQ domain. We further propose a content-aware loss to guide the enhancement process with wavelet-based pixel-level and multi-encoder-based feature-level constraints. Additionally, as a key motivation for performing image enhancement is to make the enhanced images serve better for downstream tasks, we propose a bi-level learning scheme to optimize the UMIE task and downstream tasks cooperatively, helping generate HQ images both visually appealing and favorable for downstream tasks. Experiments on three medical datasets, including two newly collected datasets, verify that the proposed method outperforms existing techniques in terms of both enhancement quality and downstream task performance. We will make the code and the newly collected datasets publicly available for community study. Copyright © 2023, The Authors. All rights reserved.

关键词： Image enhancement

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