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CHI conference on Human Factors in Computing Systems (CHI)

作者： Miranda, Fabio Hosseini, Maryam Lage, Marcos Doraiswamy, Harish Dove, Graham Silva, Claudio T. NYU New York NY 10003 USA Rutgers State Univ New Brunswick NJ USA Univ Fed Fluminense Niteroi RJ Brazil

ISBN: (纸本)9781450367080

Urban planning is increasingly data driven, yet the challenge of designing with data at a city scale and remaining sensitive to the impact at a human scale is as important today as it was for Jane Jacobs. We address this challenge with Urban Mosaic, a tool for exploring the urban fabric through a spatially and temporally dense data set of 7 7 million street-level images from New York City, captured over the period of a year. Working in collaboration with professional practitioners, we use Urban Mosaic to investigate questions of accessibility and mobility, and preservation and retrofitting. In doing so, we demonstrate how tools such as this might provide a bridge between the city and the street, by supporting activities such as visual comparison of geographically distant neighborhoods, and temporal analysis of unfolding urban development.

关键词： Urban planning Interactive visualization data analysis Urban data

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A system approach for closed-loop assessment of neuro-visual function based on convolutional neural network analysis of EEG signals

A system approach for closed-loop assessment of neuro-visual...

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conference on Neurophotonics part of SPIE Photonics Europe conference

作者： Stock, Simon C. Armengol-Urpi, Alexandre Kovacs, Balint Maier, Heiko Gerdes, Marius Stork, Wilhelm Sarma, Sanjay E. Karlsruhe Inst Technol Karlsruhe Germany MIT Cambridge MA 02139 USA

ISBN: (数字)9781510634930

ISBN: (纸本)9781510634930;9781510634923

We propose a generalized, modular, closed-loop system for objective assessment of human visual parameters. Our system presents periodical visual stimuli to the patient's field of view and analyses the consequent evoked brain potentials elicited in the occipital lobe and recorded through EEG. The analysis of the monitored EEG data is performed in an end-to-end fashion by a convolutional neural network (CNN). We propose a novel CNN architecture for EEG signal analysis that can be trained utilizing the benefits of multi-task learning. The closedloop attribute of our system allows for a real-time adaptation of the subsequent stimuli to further examine a potentially damaged area or increase the granularity of the exploration. Interchangeability is provided in terms of software modules, stimulus type, visual hardware, EEG acquisition device and EEG electrodes. Initially, the system is designed to monitor visual field loss originating from glaucoma or damage to the optic nerve using a virtual reality (VR) headset for the stimuli presentation. The modular architecture of our system paves the way for the assessment and monitoring of other neuro-visual functions.

关键词： Neuroscience Brain-Computer-Interface EEG Ophthalmology Deep Learning Convolutional Neural Networks System Design

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An Augmented Reality Mobile Application for Skin Lesion data visualization 24

An Augmented Reality Mobile Application for Skin Lesion Data...

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24th International conference Information visualisation (iv)

作者： Francese, Rita Frasca, Maria Risi, Michele Tortora, Genoveffa Univ Salerno Dept Comp Sci Salerno Italy

ISBN: (纸本)9781728191348

Melanoma is the deadliest form of skin cancer. It mainly requires a visual diagnosis by dermatologists. However, a dermatologist's recognition of melanoma may be subject to errors and may take some time to diagnose correctly it. To this aim, in the last twenty years, Computer-Aided Diagnosis systems based on artificial vision are increasingly adopted to support dermatologists in the early diagnosis of melanoma. However, these systems exploits only a reduced set of parameters or they implement a melanoma classifier that tries to substitute the dermatologists, without supporting their experience in the classification of skin lesions. This paper proposes a mobile application for supporting the clinician decision in the diagnosis of melanoma directly in the dermatologist environment by using Augmented Reality technology. In particular, computer-generated perceptual information is added to the image of patient skin reporting the values of various parameters and the lesion classification based on deep learning approach for analyzing skin lesions and identifying melanoma.

关键词： Image analysis Augmented Reality Melanoma Neural Network

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Argo Lite: Open-Source Interactive Graph exploration and visualization in Browsers 20

Argo Lite: Open-Source Interactive Graph Exploration and Vis...

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29th ACM International conference on Information and Knowledge Management (CIKM)

作者： Li, Siwei Zhou, Zhiyan Upadhayay, Anish Shaikh, Omar Freitas, Scott Park, Haekyu Wang, Zijie J. Routray, Susanta Hull, Matthew Chau, Duen Horng Georgia Inst Technol Atlanta GA 30332 USA

ISBN: (纸本)9781450368599

Graph data have become increasingly common. visualizing them helps people better understand relations among entities. Unfortunately, existing graph visualization tools are primarily designed for single-person desktop use, offering limited support for interactive web-based exploration and online collaborative analysis. To address these issues, we have developed ARGO LITE, a new in-browser interactive graph exploration and visualization tool. ARGO LITE enables users to publish and share interactive graph visualizations as URLs and embedded web widgets. Users can explore graphs incrementally by adding more related nodes, such as highly cited papers cited by or citing a paper of interest in a citation network. ARGO LITE works across devices and platforms, leveraging WebGL for high-performance rendering. ARGO LITE has been used by over 1,000 students at Georgia Tech's data and visual Analytics class. ARGO LITE may serve as a valuable open-source tool for advancing multiple CIKM research areas, from data presentation, to interfaces for information systems and more.

关键词： Interactive graph visualization user interface design network analysis

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Quality Matters: Understanding the Impact of Incomplete data on visualization Recommendation 31st

Quality Matters: Understanding the Impact of Incomplete Data...

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31st International conference on database and Expert Systems Applications (DEXA)

作者： Mafrur, Rischan Sharaf, Mohamed A. Zuccon, Guido Univ Queensland Brisbane Qld Australia United Arab Emirates Univ Al Ain U Arab Emirates

ISBN: (纸本)9783030590031;9783030590024

Incomplete data is a crucial challenge to data exploration, analytics, and visualization recommendation. Incomplete data would distort the analysis and reduce the benefits of any data-driven approach leading to poor and misleading recommendations. Several data imputation methods have been introduced to handle the incomplete data challenge. However, it is well-known that those methods cannot fully solve the incomplete data problem, but they are rather a mitigating solution that allows for improving the quality of the results provided by the different analytics operating on incomplete data. Hence, in the absence of a robust and accurate solution for the incomplete data problem, it is important to study the impact of incomplete data on different visual analytics, and how those visual analytics are affected by the incomplete data problem. In this paper, we conduct a study to observe the interplay between incomplete data and recommended visual analytics, under a combination of different conditions including: (1) the distribution of incomplete data, (2) the adopted data imputation methods, (3) the types of insights revealed by recommended visualizations, and (4) the quality measures used for assessing the goodness of recommendations.

关键词： Incomplete data visualization recommendation data exploration

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Optimizing Fenugreek Leaf Disease Detection: An exploration of Federated Learning with CNN

Optimizing Fenugreek Leaf Disease Detection: An Exploration ...

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International conference on Computing and Networking Technology (ICCNT)

作者： Shiva Mehta Vinay Kukreja Vikrant Sharma Manika Manwal Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Computer Science and Engineering Graphic Era Hill University Dehradun Uttarakhand India Computer Science and Engineering Graphic Era Deemed to be University Dehradun Uttarakhand India

Fenugreek leaf diseases severely hamper agriculture production; thus, effective detection and classification techniques are required. This study describes a novel method for identifying six kinds of fenugreek leaf diseases using federated learning and convolutional neural networks (CNNs) across several client datasets and bypassing the need for raw data exchange to retain the Federated Learning principle of privacy preservation while ensuring accurate illness categorization. Precision, recall, F1-score, and accuracy were the four main metrics to assess the model's robustness. The model consistently performed well in individual client evaluations (part iv), with F1-scores for all classes reaching 88% in Client A, 92% in Client B, 93% in Client C, and 95% in Client D. On the globally aggregated dataset (part V), the model continued to perform well, with overall F1-scores of 91.78% for Client A, 95.67% for Client B, 93.52% for Client C, and 94.93% for Client D. Three averaging techniques—macro average, weighted average, and micro average—were used for a deeper analysis in Section VI. These averaging methods demonstrated consistently good performance across all customers, with micro and weighted averages falling between 91.98% and 95.71% and macro averages ranging from 91.80% to 95.67%. These findings highlight the model's strong performance against class imbalances across several illness classes. This study highlights the potential of CNNs and federated learning for identifying and categorising agricultural diseases. The method provides a scalable, effective, and privacy-preserving alternative with a lot of promise for increasing agricultural production, particularly for the cultivation of fenugreek.

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Immersive Process Model exploration in Virtual Reality

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IEEE TRANSACTIONS ON visualIZATION AND COMPUTER GRAPHICS 2020年第5期26卷 2104-2114页

作者： Zenner, Andre Makhsadov, Akhmajon Klingner, Soren Liebemann, David Kruger, Antonio German Res Ctr Artificial Intelligence DFKI Saarland Informat Campus Saarbrucken Germany

In many professional domains, relevant processes are documented as abstract process models, such as event-driven process chains (EPCs). EPCs are traditionally visualized as 2D graphs and their size varies with the complexity of the process. While process modeling experts are used to interpreting complex 2D EPCs, in certain scenarios such as, for example, professional training or education, also novice users inexperienced in interpreting 2D EPC data are facing the challenge of learning and understanding complex process models. To communicate process knowledge in an effective yet motivating and interesting way, we propose a novel virtual reality (VR) interface for non-expert users. Our proposed system turns the exploration of arbitrarily complex EPCs into an interactive and multi-sensory VR experience. It automatically generates a virtual 3D environment from a process model and lets users explore processes through a combination of natural walking and teleportation. Our immersive interface leverages basic gamification in the form of a logical walkthrough mode to motivate users to interact with the virtual process. The generated user experience is entirely novel in the field of immersive data exploration and supported by a combination of visual, auditory, vibrotactile and passive haptic feedback. In a user study with $\mathrm{N}=27$ novice users, we evaluate the effect of our proposed system on process model understandability and user experience, while comparing it to a traditional 2D interface on a tablet device. The results indicate a tradeoff between efficiency and user interest as assessed by the UEQ novelty subscale, while no significant decrease in model understanding performance was found using the proposed VR interface. Our investigation highlights the potential of multi-sensory VR for less time-critical professional application domains, such as employee training, communication, education, and related scenarios focusing on user interest.

关键词： Virtual reality Business Virtual reality multi-sensory feedback passive haptics immersion business process models immersive data analysis

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ParaGlyder: Probe-driven Interactive visual analysis for Multiparametric Medical Imaging data 37th

ParaGlyder: Probe-driven Interactive Visual Analysis for Mul...

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37th Computer Graphics International (CGI) conference

作者： Morth, Eric Haldorsen, Ingfrid S. Bruckner, Stefan Smit, Noeska N. Univ Bergen Dept Informat Bergen Norway Haukeland Hosp Mohn Med Imaging & Visualizat Ctr Bergen Norway Univ Bergen Dept Clin Med Bergen Norway

ISBN: (纸本)9783030618636;9783030618643

Multiparametric imaging in cancer has been shown to be useful for tumor detection and may also depict functional tumor characteristics relevant for clinical phenotypes. However, when confronted with datasets consisting of multiple values per voxel, traditional reading of the imaging series fails to capture complicated patterns. These patterns of potentially important imaging properties of the parameter space may be critical for the analysis, but standard approaches do not deliver sufficient details. Therefore, in this paper, we present an approach that aims to enable the exploration and analysis of such multiparametric studies using an interactive visual analysis application to remedy the trade-offs between details in the value domain and in spatial resolution. This may aid in the discrimination between healthy and cancerous tissue and potentially highlight metastases that evolved from the primary tumor. We conducted an evaluation with eleven domain experts from different fields of research to confirm the utility of our approach.

关键词： Medical visualization visual analysis Multiparametric medical imaging data

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Literature analysis on the research status of mass entrepreneurship and innovation training projects : Document metrology based on CNKI database

Literature analysis on the research status of mass entrepren...

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Education, Knowledge and Information Management (ICEKIM), International conference on

作者： Liang Zhou Wen Jing Zhang Information Engineering Institute Lanzhou University of Finance and Economics Lanzhou China Financial Institute Lanzhou University of Finance and Economics Lanzhou China

ISBN: (纸本)9781665416672

Taking the literature of CNKI as the data source and using the literature metrological visual analysis function of CNKI, this paper makes a quantitative analysis on the relevant literature in the research field of "College Students’ innovation and entrepreneurship training program" from 2011 to 2021 from the aspects of literature quantity and annual distribution, subject analysis, discipline distribution, source journals, and author distribution, Get the research status and development trend of innovation and entrepreneurship training projects. The results show that in recent ten years, Chinese scholars have paid more and more attention to the research of "College Students’ innovation and entrepreneurship training program," and the keywords with high frequency are "innovation and entrepreneurship," "innovation and entrepreneurship education," "practical teaching" and so on; Education and teaching forum, with 4705 papers published as relevant research papers, has the most significant number of papers, followed by education modernization and laboratory research and exploration, with 393 and 333 papers respectively. Huang Suoyi, Jian Guoming, Zhang Liucheng, and the other three authors wrote a lot, but the distribution of authors was scattered. Higher education, medical education, marginal medical disciplines, computer software, and computer applications ranked among the top three in the number of documents issued.

关键词： Training Technological innovation visualization Statistical analysis Soft sensors Entrepreneurship Innovation management

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What Hides Chile’s Electricity Mix data: a data Science Perspective

What Hides Chile’s Electricity Mix Data: a Data Science Per...

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IEEE International conference on Automatica (ICA-ACCA)

作者： Javier Soto Michaol Moore Xaviera A. López-Cortés Ruber Hernández-García iván Merino Escuela de Ingeniería Civil Informática Universidad Católica del Maule Talca Chile Departamento de Computación e Industrias Facultad de Ciencias de la Ingeniería Universidad Católica del Maule Talca Chile Centro de Investigación de Estudios Avanzados del Maule and Laboratorio de Investigaciones Tecnológicas en Reconocimiento de Patrones Universidad Católica del Maule Talca Chile

ISBN: (纸本)9781665494090

In the context of decarbonization and fossil fuel independence, Chile has a great task ahead. The most relevant is to become carbon neutral by 2050 by replacing fossil fuels with clean energy sources, where, besides hydraulics, solar and wind power are the chosen ones. However, the commitment to rely on intermittent sources requires a variety of studies to support the roadmap and the success of this decarbonization plan. Therefore, this study aims to understand the behavior of the energy mix and its sources by analyzing the historical production data according to the geographical context. This way, as a starting point, it was possible to discover the main behaviors and patterns, such as the electric sources’ trends and seasonalities that explain the use of fossil fuels. To this end, data science tools were used for the exploration of big data, their respective analysis, and subsequent projections for planning the energy matrix’s decarbonization process for 2050.

关键词： Process control Low-carbon economy Production data science Wind power generation Market research Fossil fuels

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