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

共 1363 条记录，以下是1161-1170 订阅

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A Lite Fireworks Algorithm with Fractal Dimension Constraint for Feature Selection

A Lite Fireworks Algorithm with Fractal Dimension Constraint...

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Industrial Cyber-Physical Systems (ICPS)

作者： Min Zeng Haimiao Mo Zhiming Liang Hua Wang School of Computer Science Guangdong University of Science and Technology Dongguan Guangdong China School of Management Hefei University of Technology Hefei Anhui China Department of Pharmacy Fifth Affiliated Hospital of Guangzhou Medical University Guangzhou Guangdong China

As the use of robotics becomes more widespread, the huge amount of vision data leads to a dramatic increase in data dimensionality. Although deep learning methods can effectively process these high-dimensional vision data. Due to the limitation of computational resources, some special scenarios still rely on traditional machine learning methods. However, these high-dimensional visual data lead to great challenges for traditional machine learning methods. Therefore, we propose a Lite Fireworks Algorithm with Fractal Dimension constraint for feature selection (LFWA+FD) and use it to solve the feature selection problem driven by robot vision. The “LFWA+FD” focuses on searching the ideal feature subset by simplifying the fireworks algorithm and constraining the dimensionality of selected features by fractal dimensionality, which in turn reduces the approximate features and reduces the noise in the original data to improve the accuracy of the model. The comparative experimental results of two publicly available datasets from UCI show that the proposed method can effectively select a subset of features useful for model inference and remove a large amount of noise noise present in the original data to improve the performance.

关键词： Fireworks algorithm visualization Robot vision systems Sensor fusion Feature extraction Fractals data models

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Accelerating Big data Application by Eliminating Redundancy on Hadoop Cluster

Accelerating Big Data Application by Eliminating Redundancy ...

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International Conference on Parallel and Distributed Systems (ICPADS)

作者： Kelun Lei Shaokang Du Xin You Zhibo Xuan Haoran Kong Hailong Yang Jing Shang Zhiwen Xiao Zhihui Wu Zhongzhi Luan Depei Qian School of Computer Science and Engineering Beihang University Beijing China China Mobile Information Technology Center China

Big data applications are widely adopted to mine valuable information from a tremendous amount of industry data, which is commonly represented as a series of map-reduce operations. Among various map-reduce frameworks, Hadoop is most commonly adopted for data processing at large scale. Although Hadoop eases the development of highly scalable distributed big data applications, inefficient implementation due to poor coding practice and deep software abstractions can cause severe performance issues such as unreasonable slowdown, high response latency, and waste of computing resources, which can lead to unsatisfactory serving delay or significant maintenance cost. In this paper, we first categorize three common types of redundant patterns in big data applications. Then we propose a tool-assisted optimization workflow to detect the redundant patterns automatically, which profiles the application by sampling hardware performance monitoring units. Moreover, we present a profiling visualization method that can help to pinpoint the redundant codes. Based on these approaches, we optimize several big data applications by eliminating redundancies, yielding up to 14.8% performance improvement.

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Wildfire Spread Prediction in North America Using Satellite Imagery and Vision Transformer

Wildfire Spread Prediction in North America Using Satellite ...

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Artificial Intelligence (CAI), ieee Conference on

作者： Bronte Sihan Li Ryan Rad Khoury College of Computer Science Northeastern University

ISBN: (数字)9798350354096

ISBN: (纸本)9798350354102

The recent surge in wildfire incidents, exacerbated by climate change, demands effective prediction and monitoring solutions. This study introduces a novel deep learning model, the Attention Swin U-net with Focal Modulation (ASUFM), designed to predict wildfire spread in North America using large-scale remote sensing data. The ASUFM model integrates spatial attention and focal modulation into the Swin U-net architecture, significantly enhancing predictive performance on the Next Day Wildfire Spread (NDWS) benchmark. Additionally, we have developed an expanded dataset that encompasses wildfire data across North America from 2012 to 2023, providing a more extensive basis for model training and validation. Our approach demonstrates state-of-the-art performance in wildfire spread prediction, offering a promising tool for risk mitigation and resource allocation in wildfire management. Related code is in our public repository https://***/bronteee/fire-asufm

关键词： Climate change Wildfires Satellite images visualization Predictive models Environmental monitoring Remote sensing Deep learning Risk management

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Incorporating Visual Information Reconstruction into Progressive Learning for Optimizing audio-visual Speech Enhancement

Incorporating Visual Information Reconstruction into Progres...

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

作者： Chen-Yue Zhang Hang Chen Jun Du Bao-Cai Yin Jia Pan Chin-Hui Lee University of Science and Technology of China China iFlytek Research iFlytek Co. Ltd. China Georgia Institute of Technology USA

Video information has been widely introduced to speech enhancement as its contribution at low signal-to-noise ratios (SNRs). Conventional audio-visual speech enhancement networks take noisy speech and video as input and learn features of clean speech directly. To reduce the large SNR gap between the learning target and input noisy speech, we propose a novel mask-based audio-visual progressive learning speech enhancement (AVPL) framework with visual information reconstruction (VIR) to increase SNRs gradually. Each stage of AVPL takes a concatenation of pre-trained visual embedding and the previous representation as input and predicts a mask with the intermediate representation of the current stage. To extract more visual information and deal with the performance distortion, the AVPL-VIR model reconstructs the visual embedding as it is fed in for each stage. Experiment on the TCD-TIMIT dataset shows that the progressive learning method significantly outperforms direct learning for both audio-only and audio-visual models. Moreover, by reconstructing video information, the VIR module provides a more accurate and comprehensive representation of the data, which in turn improves the performance of both AVDL and AVPL.

关键词： Learning systems visualization Acoustic distortion Speech enhancement Acoustics Noise measurement data mining

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Electronic Nose with Augmented Reality visualization For Odor Classification

Electronic Nose with Augmented Reality Visualization For Odo...

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ieee International Conference on Consumer Electronics-Taiwan (ICCE-TW)

作者： Shou-Hsuan Chen Hsing-Yi Chen Ru-Ying Li Pei-Xuan Wang Min-Hsuan Yan Department of Data Science Soochow University Taipei Taiwan

The electronic nose imitates human smell mechanism and has been used in many diverse fields for odor detection. Our approach involves utilizing multiple gas sensors integrated with the Arduino platform and constructing a multivariate analysis pipeline using time series data from these sensors. Additionally, we incorporate augmented reality technology to overlay virtual objects representing predicted results onto real-time captured images. By conducting experiments using our collected odor sensor data, our approach demonstrates a performance measurement of 86% F1. These results indicate that our analysis techniques, encompassing feature engineering, dimensional reduction, and machine learning methods, possess strong discriminative capabilities for odor classification.

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Performance evaluation of the multiclass classification of flowers on diverse datasets

Performance evaluation of the multiclass classification of f...

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2023 International Conference on Electrical, Computer, Communications and Mechatronics Engineering, ICECCME 2023

作者： Hai, Phong Bui Anh Tuan, Dinh Nam, Hoang Nhat Nguyen Bao, Do Hanoi Architectural University Faculty of Information Technology Hanoi Viet Nam Hanoi University of Science and Technology School of Electrical and Electronic Engineering Hanoi Viet Nam Hanoi Viet Nam George School Ho Chi Minh City Viet Nam

ISBN: (纸本)9798350322972

The accurate classification of flowers plays an important role in our life and researches. Due to the high variation of types and species of flowers, the classification is a challenging task. So far, several classification methods have been proposed. However, the accuracy of the flower classification still needs to be investigated and improved. The paper presents the performance evaluation of flower classification. Both handcrafted feature extraction, traditional machine learning classifiers (e.g., k Nearest Neighbors, Support Vector Machine and Random Forest) and deep neural networks (e.g., Alexnet, Resnet, Inception and Densenet) have been employed to improve the accuracy of the classification. The strategies of using the data augmentation are applied to improve the performance of the classification. The classification methods have been evaluated on four public datasets (Oxford-17, Oxford-102, Kaggle-5 and Iris flower datasets) to compare and analyse the strengths and weaknesses. © 2023 ieee.

关键词： Classification (of information)

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Cascaded Cross Attention for Review-based Sequential Recommendation

Cascaded Cross Attention for Review-based Sequential Recomme...

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ieee International Conference on data Mining (ICDM)

作者： Bingsen Huang Jinwei Luo Weihao Du Weike Pan Zhong Ming College of Computer Science and Software Engineering Shenzhen University Shenzhen China Tencent Music Entertainment Shenzhen China Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ) Shenzhen University Shenzhen China

In recent years, sequential recommendation (SR) has gained significant attention in the recommender systems community. However, most previous works only consider the (user, item, timestep) interaction sequences, which limits the recommendation performance. To overcome this limitation, some studies have utilized user reviews to enrich the understanding of user preferences. However, existing review-based sequential recommendation (RBSR) methods only use either a user’s review on items or an item’s reviews by users, overlooking their complementary nature. In addition, most existing RBSR methods use a simple dot-product operation between the embeddings of a user and the candidate items for scoring, which may not adequately capture the complex relationships among the item sequence, review sequence and candidate items. To release the potential of RBSR, we propose a novel model called cascaded cross attention (CCA), which utilizes aggregated reviews to compensate for the information that is lacking in individual reviews. Moreover, we propose a cascaded cross-attention layer to better capture the dependency intra a sequence and the relationships between a sequence and the candidate items. Extensive experimental results on three public datasets demonstrate that our CCA outperforms the state-of-the-art methods. Additionally, the case study and visualization results showcase high interpretability of our CCA.

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Microstate analysis of GABAB and mGluR Mediated Modulation of Calcium Spiking in Hippocampal Neurons

Microstate analysis of GABAB and mGluR Mediated Modulation o...

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International ieee/EMBS Conference on Neural Engineering, CNE

作者： Dipanjan Sehanobish Vaibhav Dhyani Suman Gare Kishalay Mitra Lopamudra Giri Department of Chemical Engineering Bioimaging and Data Analysis Lab Indian Institute of Technology Hyderabad Sangareddy Telangana India Optical Science Centre Faculty of Science Engineering Technology Swinburne University of Technology Melbourne Australia

Drug discovery for CNS disorders is a uniquely challenging endeavor, where calcium $(\text{Ca}^{2+})$ imaging is being actively studied to evaluate the effect of GPCR-targeting drugs in vivo and in vitro. However, the quantification of drug efficacy and targetting corresponding to drug dose and drug type remains challenging. The heterogeneity in the population behavior makes it difficult to decipher the distributions of patterns corresponding to lower and higher drug doses. Herein, we demonstrate the quantification and visualization of $\text{Ca}^{2+}$ . imaging data using microstate analysis. This method generalizes the various states of the system at various points in time into fewer defined numbers of microstates appearing in the given data. These microstates and the pattern of their appearance helps to study complex system with a large number of spiking states. Such microstate decomposition can help us to understand and compare the spatiotemporal data. Using this approach, we have attempted to study the heterogeneity in $\text{Ca}^{2+}$ -imaging data obtained from live-cell imaging of rat hippocampal neurons. We have investigated how the nature of microstates changes with respect to drug treatment, size of cell population, and cell sparsity. This analysis can be useful in drug discovery and comparison of the efficacy of different doses of the drug.

关键词： Drugs Calcium Sociology Neurons Imaging Neural engineering Modulation

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Residual Hybrid Attention Network for Compression Artifact Reduction

Residual Hybrid Attention Network for Compression Artifact R...

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

作者： Bingchun Luo Wei Yu School of Computer Science and Technology Harbin Institute of Technology Weihai

Residual Hybrid Attention Network (RHAN) can restore images of arbitrary compression quality through flexibly fusing high-frequency features in the spatial and frequency domains based on the input quality factor. Specifically, to remove the compression artifacts, we propose a hybrid attention block (HAB) to adaptively restore the loss of high-frequency components, which parallelly predicts attention maps along two separate dimensions spatial and frequency spectra. To recover the compressed image flexibly and controllably, we further design a modulation decompression block (MDB), which employs a prior factor to learn a pair of modulation parameters and performs adaptively affine transformation on the obtained high-frequency features, thereby achieving high-quality image restoration at arbitrary compression levels. The quantitative and qualitative experiments on various public data sets show that the RHAN achieves the best performance and optimal visual perceptual quality in the JPEG image restoration with arbitrary compression levels.

关键词： Q-factor visualization Adaptation models Image coding Frequency modulation Frequency-domain analysis Transform coding

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Prediction and Intervention in the Effect of Online Learning in the Context of Learning Analytics

Prediction and Intervention in the Effect of Online Learning...

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International Conference on Educational and Information Technology (ICEIT)

作者： Zeping Wu Yu Zhen Bai School of Information and Science Technology Yunnan Normal University Kunming China

data-driven learning analytics can optimise the learning process and improve learning effect, but most current research focuses on the instrumental rationality of learning analytics at the expense of its value rationality. Based on learning analytics, this study applies spearman correlation analysis and multiple linear regression analysis to extract key variables that affect learning effect, build the best predictive model to identify potential risk takers, and cluster learning groups with similar behaviours. Based on the characteristics of the learning groups, three personalised teaching interventions, namely precise teacher intervention, visualisation of learning outcomes and collaborative construction of knowledge, are established to promote the learning effect of the learning groups and also reflect the value rationality of learning analysis.

关键词： visualization Correlation Instruments Linear regression Education Collaboration Predictive models

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