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36th ieee International symposium on Computer-Based Medical Systems (CBMS)

作者： Hieber, Daniel Prokop, Georg Karthan, Maximilian Holl, Felix Kestler, Hans A. Grambow, Gregor Maerkl, Bruno Pryss, Rudiger Liesche-Starnecker, Friederike Schobel, Johannes Neu Ulm Univ Appl Sci Inst DigiHlth Neu Ulm Germany Univ Augsburg Fac Med Pathol Augsburg Germany Univ Wurzburg Inst Clin Epidemiol & Biometry Wurzburg Germany Ulm Univ Inst Med Syst Biol Ulm Germany Tech Univ Munich Munich Germany Aalen Univ Appl Sci Aalen Germany

ISBN: (纸本)9798350312249

Glioblastoma is the most common malignant brain tumor with a poor survival rate due to its high intra- and intertumor heterogeneity. The current heterogeneity determination is based on a microscopic analysis of Hematoxilyn and Eosinstained tumor slides carried out by experienced neuropathologists. There is no standardized procedure yet, to quantify heterogeneity, though. With the hypothesis that the amount of heterogeneity impacts overall survival, we aim to develop an objective method to capture heterogeneity. We were able to successfully implement an initial Machine Learning classification model for determining heterogeneity. However, the available dataset was insufficient to train a resilient and stable system. Therefore, we propose an architecture for a semi-automatic data collection and preprocessing framework easing the collection of large quantities of required data. While there exists a multitude of frameworks tackling parts of the tumor research area, no simple ready-to-use solution is present for the easy collection of tumor data in daily clinical routine, especially for high-resolution pathological images. We plan to implement the proposed architecture at the University Hospital Augsburg, Germany in 2023. The dataset created in this process will then be used as a resilient basis for heterogeneity classification and for analyzing glioblastomas in general.

关键词： Tumor Classification Tumor Segmentation Machine Learning Glioblastoma data Acquisition

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On the visualization of Hierarchical Multivariate data 14

On the Visualization of Hierarchical Multivariate Data

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14th ieee Pacific visualization symposium (ieee PacificVis)

作者： Zheng, Boyan Sadlo, Filip Heidelberg Univ Heidelberg Germany

ISBN: (纸本)9781665439312

In this paper, we study the visual design of hierarchical multivariate data analysis. We focus on the extension of four hierarchical univariate concepts-the sunburst chart, the icicle plot, the circular treemap, and the bubble treemap-to the multivariate domain. Our study identifies several advantageous design variants, which we discuss with respect to previous approaches. and whose utility we evaluate with a user study and demonstrate for different analysis purposes and different types of data.

关键词： Human-centered computing visualization visualization techniques Treemaps Human-centered computing visualization visualization application domains Information visualization

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Real-Time data Processing Techniques for a Scalable Spatial and Temporal Dimension Reduction 21

Real-Time Data Processing Techniques for a Scalable Spatial ...

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21st International symposium on INFOTEH-JAHORINA (INFOTEH)

作者： Gavric, Aleksandar Vujoscvic, Dusan Radosavljevic, Nemanja Prvulovic, Petar Union Univ Sch Comp Belgrade Serbia

ISBN: (纸本)9781665437783

Wireless sensor networks (WSN) often generate data with high frequency per unit time. Values from sensor measurements are often redundant and provide little or no information. Methods of dimension reduction can be applied and thus only data of interest can be preserved for the purpose of effective analysis of wireless sensor networks' data. The authors show experimentally that it is possible to build more successful predictive models by reducing dimensions and discuss the potential advantages of reducing the spatial and temporal dimensions of sensor measurements in different applications. Authors present the implementation and analysis of an efficient distributed system that enables search, ranking, indexing, machine learning analysis and visualization of data from WSNs, processed in real-time.

关键词： Dimension reduction real-time analysis data aggregation data stream clustering

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Big data analysis of Canadian Drivers using OBDII Sensor data: The impact of the Pandemic 18

Big Data Analysis of Canadian Drivers using OBDII Sensor Dat...

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18th ieee Sensors Applications symposium (SAS)

作者： Wallace, Bruce Schonewille, Abigael Van Benthem, Kathleen Herdman, Chris Keillor, Jocelyn Goubran, Rafik Knoefel, Frank Marshall, Shawn Carleton Univ Syst & Comp Engn Ottawa ON Canada Bruyere Res Inst Ottawa ON Canada SAM 3 Innovat Hub Ottawa ON Canada Carleton Univ Dept Cognit Sci Ottawa ON Canada Natl Res Council Canada Flight Res Lab Aerosp Res Ctr Ottawa ON Canada Elisabeth Bruyere Hosp Ottawa ON Canada Carleton Univ Ottawa Hosp Ottawa ON Canada

ISBN: (纸本)9798350323078

Techniques to quantify driving skills and driving habits (e.g. duration and frequency of driving) are critical to the analysis of driving safety. Metrics related to driving are key to the analyses of safety topics such as differences in driving throughout the lifespan. An extensive literature examines such factors as novice/expert differences or the declines in driving ability that may occur with cognitive aging. Big data analysis techniques can now be applied to the datasets being captured by automakers and insurance companies through in-car recording sensors to better understand changes in driving activity and patterns. These datasets include very large samples of drivers of all ages enabling the comparative study of all drivers including comparison of novice, and older driver groups. This paper leverages one such dataset to examine the driving patterns of Canadian drivers over one year while COVID 19 pandemic precautions were in effect. Specifically, one year of data for 7677 vehicles in Canada that have a single insured driver is examined. The dataset was captured during the pandemic and a general reduction in driving associated with pandemic precautions was observed. The report suggests that for several age groups, reductions in driving were larger for males than females.

关键词： Big data Driving Driving Behavior Driving Assessment

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FiberStars: Visual Comparison of Diffusion Tractography data between Mutiple Subjects 14

FiberStars: Visual Comparison of Diffusion Tractography Data...

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14th ieee Pacific visualization symposium (ieee PacificVis)

作者： Franke, Loraine Weidele, Daniel Karl, I Zhang, Fan Cetin-Karayumak, Suheyla Pieper, Steve O'Donnell, Lauren J. Rathi, Yogesh Haehn, Daniel Univ Massachusetts Boston MA 02125 USA IBM Res Armonk NY USA Harvard Med Sch Boston MA 02115 USA Isomics Inc Cambridge MA USA

ISBN: (纸本)9781665439312

Tractography from high-dimensional diffusion magnetic resonance imaging (dMRI) data allows brain's structural connectivity analysis. Recent dMRI studies aim to compare connectivity patterns across subject groups and disease populations to understand subtle abnormalities in the brain's white matter connectivity and distributions of biologically sensitive dMRI derived metrics. Existing software products focus solely on the anatomy, are not intuitive or restrict the comparison of multiple subjects. In this paper, we present the design and implementation of FiberStars, a visual analysis tool for tractography data that allows the interactive visualization of brain fiber clusters combining existing 3D anatomy with compact 2D visualizations. With FiberStars, researchers can analyze and compare multiple subjects in large collections of brain fibers using different views. To evaluate the usability of our software, we performed a quantitative user study. We asked domain experts and non-experts to find patterns in a tractography dataset with either FiberStars or an existing dMRI exploration tool. Our results show that participants using FiberStars can navigate extensive collections of tractography faster and more accurately. All our research, software, and results are available openly.

关键词： Neuroscience Brain Fibers Diffusion MRI High-Dimensional data 3D visualization visualization techniques

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Sketching Merge Trees for Scientific visualization

Sketching Merge Trees for Scientific Visualization

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2023 Topological data analysis and visualization, TopoInVis 2023

作者： Li, Mingzhe Palande, Sourabh Yan, Lin Wang, Bei University of Utah United States Michigan State University United States Argonne National Laboratory United States

ISBN: (纸本)9798350329643

Merge trees are a type of topological descriptors that record the connectivity among the sublevel sets of scalar fields. They are among the most widely used topological tools in visualization. In this paper, we are interested in sketching a set of merge trees using techniques from matrix sketching. That is, given a large set $\mathcal{T}$ of merge trees, we would like to find a much smaller set of basis trees $S$ such that each tree in $\mathcal{T}$ can be approximately reconstructed from a linear combination of merge trees in S. A set of high-dimensional vectors can be approximated via matrix sketching techniques such as principal component analysis and column subset selection. However, until now, there has not been any work on sketching a set of merge trees. We develop a framework for sketching a set of merge trees that combines matrix sketching with tools from optimal transport. In particular, we vectorize a set of merge trees into high-dimensional vectors while preserving their structures and structural relations. We demonstrate the applications of our framework in sketching merge trees that arise from time-varying scientific simulations. Specifically, our framework obtains a set of basis trees as representatives that capture the 'modes' of physical phenomena for downstream analysis and visualization. © 2023 ieee.

关键词： Topology

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A Method for Difference analysis of Genomic data Mining in Breast Cancer

A Method for Difference Analysis of Genomic Data Mining in B...

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2023 International Conference on Applied Physics and Computing, ICAPC 2023

作者： Chen, Yihao Guo, Ling Huang, Tao College of Electrical Engineering Northwest Minzu University Gansu Engineering Research Center for Eco-Environmental Intelligent Networking Lanzhou China

ISBN: (纸本)9798350359244

The emergence of high-throughput sequencing technology provides access to large volumes of biological data. Mining important information from such a huge amount of genomic data is a big challenge and requires complicated data analysis techniques. In the present study, difference analysis technology was applied to mine breast cancer genomic data for important information about aggressiveness. Meanwhile, GO enrichment analysis and KEGG enrichment analysis were used to further extract significant information closely associated with breast cancer aggressiveness. In addition, visualization of the analysis results was provided. Finally, significant differential genes in breast cancer genomics data were obtained by differential analysis techniques and possible future perspectives are presented. © 2023 ieee.

关键词： data mining

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How IoT Re-using Threatens Your Sensitive data: Exploring the User-data Disposal in Used IoT Devices 44

How IoT Re-using Threatens Your Sensitive Data: Exploring th...

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44th ieee symposium on Security and Privacy (SP)

作者： Liu, Peiyu Ji, Shouling Fu, Lirong Lu, Kangjie Zhang, Xuhong Qin, Jingchang Wang, Wenhai Chen, Wenzhi Zhejiang Univ Hangzhou Peoples R China Zhejiang Univ NGICS Platform Hangzhou Peoples R China Univ Minnesota Minneapolis MN 55455 USA

ISBN: (纸本)9781665493369

With the rapid technology evolution of the Internet of Things (IoT) and increasing user needs, IoT device re-using becomes more and more common nowadays. For instance, more than 300,000 used IoT devices are selling on Craigslist. During IoT re-using, sensitive data such as credentials and biometrics residing in these devices may face the risk of leakage if a user fails properly dispose of the data. Thus, a critical security concern is raised: do (or can) users properly dispose of the sensitive data in used IoT? To the best of our knowledge, it is still an unexplored problem that desires a systematic study. In this paper, we perform the first in-depth investigation on the user-data disposal of used IoT devices. Our investigation integrates multiple methods to explore the status quo and the root causes of the user-data leakages with used IoT devices. First, we conduct a user study to investigate the user awareness and understanding of data disposal. Then, we conduct a largescale analysis on 4,749 IoT firmware images to investigate userdata collection. Finally, we conduct a comprehensive empirical evaluation on 33 IoT devices to investigate the effectiveness of existing data disposal methods. Through the systematical investigation, we discover that IoT devices collect more sensitive data than users expect. Specifically, we detect 121,984 sensitive data collections in the tested firmware. Moreover, users usually do not or even cannot properly dispose of the sensitive data. Worse, due to the inherent characteristics of storage chips, 13.2% of the investigated firmware perform "shallow" deletion, which may allow adversaries to obtain sensitive data after data disposal. Given the large-scale IoT re-using, such leakage would cause a broad impact. We have reported our findings to world-leading companies. We hope our findings raise awareness of the failures of user-data disposal with IoT devices and promote the protection of users' sensitive data in IoT devices.

关键词： Internet of things

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Learning a Deep Ensemble Network With Band Importance for Hyperspectral Object Tracking

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ieee TRANSACTIONS ON IMAGE PROCESSING 2023年 32卷 2901-2914页

作者： Li, Zhuanfeng Xiong, Fengchao Zhou, Jun Lu, Jianfeng Qian, Yuntao Nanjing Univ Sci & Technol Sch Comp Sci & Engn Nanjing 210094 Peoples R China Griffith Univ Sch Informat & Commun Technol Nathan Qld 4111 Australia Zhejiang Univ Coll Comp Sci Hangzhou 310027 Peoples R China

Attributing to material identification ability powered by a large number of spectral bands, hyperspectral videos (HSVs) have great potential for object tracking. Most hyperspectral trackers employ manually designed features rather than deeply learned features to describe objects due to limited available HSVs for training, leaving a huge gap to improve the tracking performance. In this paper, we propose an end-to-end deep ensemble network (SEE-Net) to address this challenge. Specifically, we first establish a spectral self-expressive model to learn the band correlation, indicating the importance of a single band in forming hyperspectral data. We parameterize the optimization of the model with a spectral self-expressive module to learn the nonlinear mapping from input hyperspectral frames to band importance. In this way, the prior knowledge of bands is transformed into a learnable network architecture, which has high computational efficiency and can fast adapt to the changes of target appearance because of no iterative optimization. The band importance is further exploited from two aspects. On the one hand, according to the band importance, each frame of HSVs is divided into several three-channel false-color images which are then used for deep feature extraction and location. On the other hand, based on the band importance, the importance of each false-color image is computed, which is then used to assemble the tracking results from individual false-color images. In this way, the unreliable tracking caused by false-color images of low importance can be suppressed to a large extent. Extensive experimental results show that SEE-Net performs favorably against the state-of-the-art approaches. The source code will be available at https://***/hscv/SEE-Net.

关键词： Hyperspectral imaging Target tracking Object tracking visualization Feature extraction Task analysis Videos hyperspectral videos learning to optimize ensemble learning

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Planning of Electric Vehicle Charging Pile for large Parking Lots

Planning of Electric Vehicle Charging Pile for Large Parking...

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2024 CPSS and ieee International symposium on Energy Storage and Conversion, CPSS and ISESC 2024

作者： Xiaobin, Zhang Lin, Shi Dianbo, Wang Yu, Wang Zhen, Ren Xuan, Zou School of Electrical Engineering Xi'An University of Technology Xian710048 China Shaanxi Electric Power Design Institute Co Ltd China Energy Engineering Group Xian710054 China

ISBN: (纸本)9798350380514

Reasonable planning of electric vehicle charging piles (EVCP) can not only improve the using experience of electric vehicle (EV) users, but also improve the related grid stability and economy, and the rate of return on investment. Now the EVCP planning is usually for larger areas at the district and county levels, but from the user side, the EVCP planning for park-level areas is more conducive to improving the enthusiasm of investors and the experience of surrounding EV users. This paper takes the large parking areas with large traffic flow as the object. Firstly, the battery capacity, parking situation, state of charge distribution and charging willingness of different types EVs in the region are obtained through the survey, and then based on the statistical analysis of these data, the MonteCarlo method is used to predict the charging load of EVs in the region. According to the grid information and the charging load of EVs in the region, the EVCP planning method is proposed with the comprehensive optimization of investment, grid line loss and user satisfaction. Finally, a large parking lot of an airport is taken as an example to verify the effectiveness of the proposed EVCP planning method. Compared with the traditional EVCP planning, this method is more economical and reasonable, and can guide the EVCP planning of practical projects. © 2024 ieee.

关键词： Charging Load Forecasting Charging Piles Planning Electric Vehicle Multi-objective Optimization

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