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检索条件"任意字段=2023 IEEE Visualization in Data Science, VDS 2023"

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Towards Developing a Mobile-Based Care (KidneyCare) for Patients with Kidney Diseases Using Ten-second Fingertip Video and PPG with Machine Learning

Towards Developing a Mobile-Based Care (KidneyCare) for Pati...

引用

Digital Health (ICDH), ieee International Conference on

作者： Parama Sridevi Masud Rabbani Sayed Mashroor Mamun Mohammad Syam Rumi Ahmed Khan Sheikh Iqbal Ahamed Computer Science Department Ubicomp Lab Marquette University Milwaukee WI USA Georgia Cancer Center Augusta University GA US Department of Internal Medicine University of Texas Austin TX USA

In this manuscript, we describe the architecture and development of a non-invasive prototype- KidneyCare that captures the ten-second fingertip video using the smartphone camera and NIR LED. Then this fingertip video will be analyzed to estimate the creatinine, GFR, and CrCl levels by using ML models. KidneyCare acts as a regular, remote, self-monitoring, and noninvasive point-of-care by providing information about the user’s kidney condition. KidneyCare provides data visualization features and conducts as a data management platform for practitioners which promotes early diagnosis and initiation of an early treatment plan. With all the above-mentioned features, KidneyCare will be a powerful mHealth-based system for monitoring kidney conditions by estimating Creatinine, GFR, and CrCl levels non-invasively.

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Mechatronic Design for Multi Robots-Insect Swarms Interactions

Mechatronic Design for Multi Robots-Insect Swarms Interactio...

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2023 ieee International Conference on Mechatronics (ICM)

作者： Fatemeh Rekabi-Bana Martin Stefanec Jiří Ulrich Erhan E. Keyvan Tomáš Rouček George Broughton Bilal Y. Gündeǧer Ömer Sahin Ali E. Turgut Erol Şahin Tomáš Krajník Thomas Schmickl Farshad Arvin Department of Computer Science Swarm & Computational Intelligence Laboratory (SwaCIL) Durham University UK Artificial Life Lab University of Graz Austria Faculty of Electrical Engineering Czech Technical University Czechia Middle East Technical University Türkiye

This paper presents the concept of a robotic system collaborating with a swarm of social insects inside their hive. This robot consists of a micro-and macro-manipulator and a tracking system. The micro-manipulator uses bio-mimetic agents to interact with an individual specimen. The macromanipulator positions and keeps the micro-manipulator’s base around the given individual while moving in the hive. This individual is tracked by a fiducial marker-based visual detection and localisation system, which also provides positions of the biomimetic agents. The base of the system was experimentally verified in a honeybee observation hive, where it flawlessly tracked the honeybee queen for several hours, gathering sufficient data to extract the behaviours of honeybee workers in the queen’s vicinity. These data were then used in simulation to verify if the micro-manipulator’s bio-mimetic agents could mimic some of the honeybee workers’ behaviours.

关键词： visualization Mechatronics Tracking databases Machine vision Insects data models

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Enhancing the Coverage of Underwater Robot Based Mn-crust Survey Area by Using a Multibeam Sonar

Enhancing the Coverage of Underwater Robot Based Mn-crust Su...

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International Symposium on Underwater Technology

作者： Umesh Neettiyath Mehul Sangekar Kazunori Nagano Tetsu Koike Blair Thornton Harumi Sugimatsu Hikari Hino Akiko Suzuki Institute of Industrial Science The University of Tokyo Komaba Japan X-STAR Japan Agency for Marine-Earth Science and Technology Yokosuka Japan Southampton Marine and Maritime Institute University of Southampton Southampton SO16 7QF UK Japan Organization for Metals and Energy Security Toranomon Japan

The authors conducted a field survey of Cobalt-rich Manganese Crusts (Mn-crusts) using a visual 3D mapping system and a multibeam sonar mounted on an underwater robot for studying the distribution of these resources on the seafloor. The multibeam sonar covers an area 4 times that of the visual system and generates bathymetric and backscatter information. This paper shows preliminary results from field trials, showing the correlations between the multibeam and visual mapping data in Mn-crust covered seamounts. Further, the possibility of characterization of the seafloor using multibeam data is investigated by classifying the multibeam data using transfer learning of a classifier trained on visual mapping data. It is advantageous because co-located visual data can be used as a ground truth for multibeam classification. Mn-crust volumetric estimation methods, currently limited to visually mapped regions, can be extended further using these results.

关键词： Support vector machines visualization Autonomous underwater vehicles Three-dimensional displays Transfer learning Sonar Visual systems

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Distribution-Consistent Modal Recovering for Incomplete Multimodal Learning

Distribution-Consistent Modal Recovering for Incomplete Mult...

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International Conference on Computer Vision (ICCV)

作者： Yuanzhi Wang Zhen Cui Yong Li PCA Lab Key Lab of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China

Recovering missing modality is popular in incomplete multimodal learning because it usually benefits downstream tasks. However, the existing methods often directly estimate missing modalities from the observed ones by deep neural networks, lacking consideration of the distribution gap between modalities, resulting in the inconsistency of distributions between the recovered and the true data. To mitigate this issue, in this work, we propose a novel recovery paradigm, Distribution-Consistent Modal Recovering (DiCMoR), to transfer the distributions from available modalities to missing modalities, which thus maintains the distribution consistency of recovered data. In particular, we design a class-specific flow based modality recovery method to transform cross-modal distributions on the condition of sample class, which could well predict a distribution-consistent space for missing modality by virtue of the invertibility and exact density estimation of normalizing flow. The generated data from the predicted distribution is integrated with available modalities for the task of classification. Experiments show that DiCMoR gains superior performances and is more robust than existing state-of-the-art methods under various missing patterns. visualization results show that the distribution gaps between recovered modalities and missing modalities are mitigated. Codes are released at https://***/mdswyz/DiCMoR.

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Realistic Volume Rendering with Environment-Synced Illumination in Mixed Reality

Realistic Volume Rendering with Environment-Synced Illuminat...

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ieee International Symposium on Mixed and Augmented Reality Workshops (ISMARW)

作者： Haojie Cheng Chunxiao Xu Xujing Chen Zhenxin Chen Jiajun Wang Lingxiao Zhao School of Biomedical Engineering University of Science and Technology of China Suzhou Institute of Biomedical Engineering and Technology Chinese Academy of Sciences

Interactive volume visualization using a mixed reality (MR) system helps provide users with an intuitive spatial perception of volumetric data. Due to sophisticated requirements of user interaction and vision when using MR head-mounted display (HMD) devices, the conflict between the realisticness and efficiency of direct volume rendering (DVR) is yet to be resolved. In this paper, a new MR visualization framework that supports interactive realistic DVR is proposed. An efficient illumination estimation method is used to identify the high dynamic range (HDR) environment illumination captured using a panorama camera. To improve the visual quality of Monte Carlo-based DVR, a new spatio-temporal denoising algorithm is designed. Based on a reprojection strategy, it makes full use of temporal coherence between adjacent frames and spatial coherence between the two screens of an HMD to optimize MR rendering quality. Several MR development modules are also developed for related devices to efficiently and stably display the DVR results in an MR HMD. Experimental results demonstrate that our framework can better support immersive and intuitive user perception during MR viewing than existing MR solutions.

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A Web-Based Learning Platform for Teaching data science to Non-Computer Majors

A Web-Based Learning Platform for Teaching Data Science to N...

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Frontiers in Education (FIE) Conference

作者： Xumin Liu Erik Golen Rajendra Raj Kimberly Fluet Department of Computer Science Rochester Institute of Technology Rochester NY School of Information Rochester Institute of Technology Rochester NY Center for Professional Development and Education Reform University of Rochester Rochester NY

A web-based learning platform is useful as it allows students with limited or no programming background to conduct in-depth hands-on practice in data science. Background: The need for data science coursework for non-computing majors has grown in recent years, given the demand in various disciplines. However, a substantial number of current data science courses are inappropriate for non-computing majors as they typically require a long chain of prerequisite courses in computer science and mathematics. Moreover, courses designed for computing majors do not match the preparation and interests of students majoring in other disciplines. Outcomes: This paper presents a platform for Learning data science (DSLP), a web-based platform, which assists in the teaching and learning of data science topics by students with limited or no coding experience, including those that have completed a high school AP Computer science Principles (CSP) class or an equivalent CSP course increasingly offered in many colleges. Application Design: The platform helps students understand fundamental data science concepts and techniques, as well as provides them with an in-depth hands-on experience that goes beyond their coding capabilities. The platform offers various data visualization supports to help students understand data and analysis results. Students can use the platform to work on in-house datasets or their own data. This allows students to focus more on how to solve data science problems in various domains than how to write code. The platform also has several unique features that make it particularly helpful for teaching and learning data science topics such as code exemplification and sandbox, informative instructions, and progress monitoring. Findings: The platform has been used multiple times in data science courses for non-computing majors offered at the authors' institution. Preliminary student feedback indicated that the platform is effective in terms of improving student understand

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A Multi-Signal Perception Network for Textile Composition Identification

A Multi-Signal Perception Network for Textile Composition Id...

引用

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

作者： Bo Peng Liren He Dong Wu Mingmin Chi Jintao Chen School of Computer Science Shanghai Key Laboratory of Data Science Fudan University Shanghai China Zhongshan Fudan Joint Innovation Center Zhongshan PoolNet Technology Co. Ltd Zhongshan China Zhengzhou Zhongke Institute of Integrated Circuit and System Application Shanghai Fabric Eyes Artificial Intelligence Technology Co. Ltd Shanghai China

Textile composition identification (TCI) is an essential basic link in the textile industry. Methods based on computer vision or near-infrared (NIR) signal processing have shown potential for the nondestructive TCI task. However, these methods ignore that the integration of NIR signals and visual information may help the model learn a better representation through information complementarity. This paper propose a Multi-Signal Perception Network (MSPNet) for nondestructive textile composition identification, allowing the model to benefit from the advantages of multimodal data. Firstly, a two-way feature extraction network is used to obtain multi-modal features. After that, we propose a multimodal signal fusion module to control the aggregation granularity among multimodal data. Specifically, the target areas of the image are perceived by a target area perception module (TAP). Then a bi-gated aggregation (Bi-GFA) is designed to capture consistent semantic information from signal to image and image to signal. The quantitative and qualitative results of the proposed MSPNet are significantly improved compared to both single and multimodal approaches.

关键词： visualization Computer vision Semantics Signal processing Feature extraction data models Task analysis

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ImagineNet: Target Speaker Extraction with Intermittent Visual Cue Through Embedding Inpainting

ImagineNet: Target Speaker Extraction with Intermittent Visu...

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

作者： Zexu Pan Wupeng Wang Marvin Borsdorf Haizhou Li Institute of Data Science National University of Singapore (NUS) Singapore Department of Electrical and Computer Engineering NUS Singapore Machine Listening Lab University of Bremen Germany Shenzhen Research Institute of Big Data School of Data Science The Chinese University of Hong Kong Shenzhen China

The speaker extraction technique seeks to single out the voice of a target speaker from the interfering voices in a speech mixture. Typically an auxiliary reference of the target speaker is used to form voluntary attention. Either a pre-recorded utterance or a synchronized lip movement in a video clip can serve as the auxiliary reference. The use of visual cue is not only feasible, but also effective due to its noise robustness, and becoming popular. However, it is difficult to guarantee that such parallel visual cue is always available in real-world applications where visual occlusion or intermittent communication can occur. In this paper, we study the audio-visual speaker extraction algorithms with intermittent visual cue. We propose a joint speaker extraction and visual embedding inpainting framework to explore the mutual benefits. To encourage the interaction between the two tasks, they are performed alternately with an interlacing structure and optimized jointly. We also propose two types of visual inpainting losses and study our proposed method with two types of popularly used visual embeddings. The experimental results show that we outperform the baseline in terms of signal quality, perceptual quality, and intelligibility.

关键词： visualization Lips Design methodology Signal processing algorithms Signal processing Noise robustness Synchronization

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Doppler Image-Based Weakly-Supervised Vascular Ultrasound Segmentation with Transformer

Doppler Image-Based Weakly-Supervised Vascular Ultrasound Se...

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ieee International Symposium on Biomedical Imaging

作者： Guochen Ning Hanying Liang Fang Chen Xinran Zhang Hongen Liao Dept. of Biomedical Engineering Tsinghua University Beijing China Dept. of Computer Science and Engineering Nanjing University of Aeronautics and Astronautics Nanjing China

Vascular segmentation in ultrasound (US) images faces labor-intensive labeling procedures and performance degradation due to unsatisfied image quality. Herein, we propose to use the Doppler image for vascular segmentation with an incremental Transformer structure. First, local features within the image patch are extracted by a convolutional neural network (CNN) patch embedding layer and further encoded by a multi-level Transformer to enhance the global dependencies from coarse to fine. A multi-level CNN decoder is introduced to decode corresponding features. Doppler imaging is capable of blood visualization and indicating the positional and structural information used as the pseudo label. A conditional random field (CRF) module and shape similarity loss function are introduced to improve the effectiveness of the Doppler images. The segmentation accuracy of the radial artery and carotid artery datasets can achieve 78.8% and 81.9% in Dice, with 63.5% and 53.7% accuracy in noisy labels. In addition, the framework can be generalized to unseen data without related Doppler images.

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An Empirical Study of Backdoor Attacks on Masked Auto Encoders

An Empirical Study of Backdoor Attacks on Masked Auto Encode...

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

作者： Shuli Zhuang Pengfei Xia Bin Li University of Science and Technology of China Hefei China

Large-scale unlabeled data has spurred recent progress in self-supervised learning methods for learning rich visual representations. Masked autoencoders (MAE), a recently proposed self-supervised method, has exhibited exemplary performance on many vision tasks by masking and reconstructing random patches of input. However, as a representation learning method, the backdoor pitfall of MAE, and its impact on downstream tasks, have not been fully investigated. In this paper, we use several common triggers to perform backdoor attacks on the pre-training phase of MAE and test them on downstream tasks. We explore some key factors such as trigger patterns and the number of poisoned samples. Some interesting results can be obtained. The pre-training process of MAE can be used to enhance the memory of the encoder for the trigger mode. The global trigger is easier than local triggers to attack the encoder. The blended ratio and patch size of the triggers have a great impact on MAE. Code is available at https://***/zhuangshuli/MAE-Backdoor.

关键词： Representation learning visualization Codes Self-supervised learning Signal processing Security Task analysis

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