[2017 AI-SOCD] AirBox Data Visualization: demonstration with case studies

The AirBox Project aims to create an ecosystem focused on participatory PM2.5 air pollution monitoring. It involves micro air pollution sensing using low-cost devices, open data analysis, app and firmware development, public awareness campaigns, and collaborations with experts, communities, and governments. Over 2,000 AirBox sensor devices have been deployed across 29 countries to measure PM2.5 levels and provide real-time data online through open data portals and dashboards.

From AirBox to Smart City: where are we and what's next?

The document discusses the AirBox project, which aims to monitor PM2.5 levels through participatory citizen sensing. It describes how over 1,600 AirBox devices have been deployed across 24 countries to measure air quality. The data is openly available through APIs and dashboards. The project also focuses on education and community engagement around air pollution issues. Applications of the large sensor network data include tracking emission sources, anomaly detection, and informing government policymaking to help make cities smarter.

2015/12/16 Participatory Urban Sensing

AirBox: a participatory ecosystem for PM2.5 monitoring

The document describes the AirBox ecosystem for participatory PM2.5 monitoring in Taiwan. It discusses how the system was built from an initial participatory sensing project in 2013 using low-cost sensors and volunteers, to the current ecosystem involving academia, communities, industry and government. The ecosystem is based on open hardware, software and data, and has expanded from Taiwan to other parts of Asia.

Livesay_CV_UTD

Jake Livesay

Ronald J Livesay is a physicist with expertise in applied physics, radiation measurement, data analysis, and nuclear security. He received his PhD in Applied Physics from Colorado School of Mines in 2007. He has over 10 years of experience at Oak Ridge National Laboratory, where he conducted research on radiation detection systems, developed data analysis techniques, and patented inventions related to radiation monitoring. He now co-founded Mason Livesay Scientific, LLC in 2012 where he leads research on radiation detector performance and data analysis automation.

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WGBMEResume

William Gottschalk

William Gottschalk graduated from The George Washington University with a Bachelor of Science in Biomedical Engineering and a minor in Biophysics. He has relevant coursework in mathematics, natural sciences, programming, and biological and electrical engineering. His technical skills include programming languages like C and C++, and software like Matlab, Pspice, and Labview. For his senior design project, he helped create a physiological measurement system embedded in clothing that could measure heart rate, respiration, distance, speed, and energy expenditure. He has research experience interning at Memorial Sloan Kettering Cancer Center and clinical experience interning at Norwalk Hospital. Currently he works as a Media Analyst at TVEyes Incorporated.

The document provides information about CITRIS and computational science and engineering (CSE) at Berkeley. It summarizes key events and initiatives in CSE including the establishment of a graduate program in CSE, the impact of multicore computing, growth of cloud computing, and XSEDE. It also outlines the Designated Emphasis in CSE, participating departments and courses, and resources available to students.

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This document discusses using Excel and R software for uncertainty analysis. It summarizes software developed by the National Institute of Standards and Technology called MetRology for performing uncertainty analysis. It also outlines topics that will be demonstrated including using Excel and R for input quantity slip-ups, sensitivity analysis, variance decomposition, and approximate attained confidence. An exercise will also be included.

Roberti: NEON's approach to uncertainty estimation for sensor-based measurem...

The document discusses the National Ecological Observatory Network's (NEON) approach to quantifying uncertainty in sensor-based measurements. NEON follows guidelines from the Joint Committee for Guides in Metrology to identify and quantify uncertainties throughout the data collection and processing chain. This includes accounting for uncertainties from sensor calibration, environmental factors, data conversion equations, and other sources. Quantifying uncertainties is important for determining statistical properties of the data and constructing higher-level data products and models. The approach aims to standardize, trace, and increase transparency around uncertainty in ecological measurement networks.

Three Laws of Trusted Data Sharing:(Building a Better Business Case for Dat...

The document discusses three laws of trusted data sharing based on research in software engineering quality prediction. The first law is to only share the essential "corners" of the data rather than all data. The second law is to anonymize the data in the corners before sharing. The third law is never to mutate the data across important "decision boundaries". The research found that building models from a small percentage of shared and privatized data in this way produced better results than using all the original raw data. The author plans to apply these laws of data sharing to other domains like smart cities and healthcare to investigate the costs and benefits of data sharing.

Big Data: the weakest link

This document discusses whether big data analysis is more of a "systems" task or "human" task. It presents research showing that software defect prediction, even when conducted by top experts using the same datasets and algorithms over many years, shows little improvement and high variability. This suggests that human factors like biases are important. The document proposes using data mining on source code and social media to classify developers by expertise and identify groups who could share knowledge to reduce defects. It outlines an initial approach using parsers, classifiers like Naive Bayes to distinguish novices from experts, and seeking larger datasets from partners. The goal is to strengthen the "human" aspects of big data analysis.

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Tutorial ESWC2011 Building Semantic Sensor Web - 01 - Introduction

Jean-Paul Calbimonte

This document outlines the objectives, schedule, and introduction for a tutorial on building semantic sensor webs and applications. The tutorial aims to teach developers and experts about building semantically-enabled applications that rely on data from sensor networks. It will cover semantic sensor web components, discovering sensor data sources for a region, querying streaming data through ontologies, and sensor data semantic mashups. The introduction discusses sensor networks as information sources, the sensor web, end users of sensor data, and challenges in sensor data management including interoperability, data quality, and distributed processing.

Materials Data in the 21st Century: From Mishmash to Moneyball

The scientific method consists of generating and analyzing data to create knowledge. Indeed, every materials scientist uses data from syntheses, characterization, and models to explain and optimize materials behavior. Yet, despite the centrality of data to progress in materials, the world’s immense body of materials data remains unstandardized, unstructured, and trapped in myriad publications, isolated repositories, and private computers. This disaggregation (the mishmash) not only prevents materials scientists from standing on the shoulders of giants, but also limits our ability to use large-scale data analytics to dramatically accelerate materials modeling, discovery, and manufacture (à la Moneyball). Citrine Informatics is a team of materials scientists dedicated to uniting all materials data on a single platform within a single data standard, and putting user-friendly, data-driven tools into the hands of all materials researchers. The company’s vision is to make the full materials R&D pipeline—from initial discovery to scale-up and commercialization—ten times faster than it is today. In this talk, we will review the present state of affairs in materials data, notable progress to date, opportunities for the future, and the challenges likely to arise along the way.

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From all comments received by the LTER Network Office, this year's meeting in Estes Park was a complete success! The 2015 LTER All Scientists Meeting was held from August 30 through the evening of September 2. The Conference was organized around the theme: " From Long-Term Data to Understanding: Toward a Predictive Ecology". Almost 600 people attended the meeting. There were over 300 poster presentations and more than 75 formal and ad-hoc working group meetings. Drs. James Olds, Diana Wall, Knute Nadelhoffer, Ned Gardener and Christine O'Connell provided excellent plenary presentations to highlight the meeting. Chloe Wardropper (NTL) won 1st place in the student poster competition with Alexandra Conway (BNZ), Shinjini Goswami (HBR), Hafsah Nahrawi (GCE) and Bonnie McGill (KBS) winning runner-up awards.

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Christoph Barrett - Policy Informatics at Societal Scale

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Towards the impact of design flaws on resources used by an application

Serban Stoenescu

This document discusses two case studies that analyze the impact of design flaws on resource usage in software applications. The first case study uses a simple example to show that a "data class" design flaw increases memory and CPU usage. The second case study analyzes the open source application JhotDraw 5.4, refactoring flaws like cyclic dependencies and data clumps, and finding that memory usage decreased in all refactored versions while CPU usage varied. The document concludes that design flaws can influence resource usage like memory and CPU time, and that further studies on other systems could provide more evidence.

Official resume titash_mandal_

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Titash Mandal is a graduate student pursuing a Master's degree in Computer Science from New York University with extensive experience in programming, technical skills, and academic projects. He has worked as a teaching assistant and lead instructor for K-12 STEM programs where he designed curriculum and taught topics in computer science and cybersecurity. His research experience includes developing algorithms for image analysis and retinal disease classification.

WebEngLab_In-YoungKo_201807

University of Illinois at Urbana-Champaign

- The document describes the Web Engineering Lab at KAIST, led by Prof. In-Young Ko. - The lab focuses on software engineering for dynamic environments like the web, cloud, IoT and CPS. This includes adaptive and evolvable software design, knowledge-based SE, and end-user SE. - The lab also works on services computing, including service-oriented architectures and composition for IoT/CPS environments.

Ci days notre_dame_april2010

Software Sustainability Institute

Neil Chue Hong

The Software Sustainability Institute (SSI) provides services to help research groups sustain their software over the long term. It collaborates with groups in various fields to improve key software through advice, training, and partnerships. Case studies describe projects in fields like fusion energy, climate modeling, geospatial data, and computational chemistry. The SSI aims to promote best practices and change perceptions so software is recognized as a valuable long-term asset, not just for initial research. Sustaining software requires support for both technical aspects and community engagement over decades.

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FACE-IT is an effort to develop a new IT infrastructure to accelerate existing disciplinary research and enable information transfer among traditionally separate fields. At present, finding data and processing it into usable form can dominate research efforts. By providing ready access to not only data but also the software tools used to process it for specific uses (e.g., climate impact and economic model inputs), FACE-IT allows researchers to concentrate their efforts on analysis. Lowering barriers to data access allows researchers to stretch in new directions and allows researchers to learn and respond to the needs of other fields. FACE-IT builds on the Globus Galaxies platform, which has been developed over the past several years at the University of Chicago. FACE-IT also benefit from substantial software development undertaken by the communities who have developed most of the domain-specific tools required to populate FACE-IT with useful capabilities. The FACE-IT Galaxy manages earth system datatypes (as NetCDF), new tool parameters (dates, map, opendap), aggregated datatypes (RAFT), service providers and cool map visualizers.

Information Technology in Industry(ITII) - November Issue 2018

ITIIIndustries

Digital Twin and Smart Spaces

SANGHEE SHIN

I gave this talk at the 'Digital Twin Conference' hosted by LH Corp at COEX, Seoul on August 8th, 2019. Abstract: 'Digital Twin' is a digital replication of real world objects, processes, phenomena that can be used for various purposes. Digital twin concept backs to manufacturing industry in early 2000s for the PLM (Product Lifecycle Management) purposes. It is based on the idea that a digital informational construct about a physical system could be created as an entity on its own. Definitions of digital twin emphasize the three important levels or characteristics. At first, there should be connection between real physical world and corresponding virtual world. To do this, Level 1 digital twin provides virtual 3D models. Secondly, this connection between real world and virtual world is established by generating (near) real time data using sensors or IoT. This is called Level 2 digital twin. Thirdly, Level 3 digital twin carries out certain analyses, predictions, and simulations using virtual 3D and (near) real time data. ‘Smart Spaces’ are interactive environments where humans and technology can openly communicate with each other in a physical or digital setting. Examples of smart spaces include smart cities, smart factories, and smart homes. ‘Smart Spaces’ is one of Garner’s Top 10 Tech Trends for 2019. As spaces are going through digital transformation with 4th industrial revolution, there are many attempts to apply digital twin technology to manage urban, spatial, and industrial issues around the world. Those attempts look set to play an increasingly important role in the creation of smart cities, smart factories, and smart homes. Bringing the virtual and real worlds together in this way can help to give better analysis, visualization, and simulation to the decision-making process. This will be a multi-way process with iterative feedback among stakeholders. In this talk, I'll share my real experiences in carrying out digital twin and smart space projects. Also I’ll talk about what I’ve learnt from these projects.

WebEng_202010

IEEE Xplore Digital Library

This document summarizes the research areas and activities of Prof. In-Young Ko's lab at KAIST. The lab focuses on software engineering for dynamic environments like the web, clouds, IoT and CPS. Key research topics include adaptive software design, microservices, semantic web applications and edge cloud computing. The lab has PhD students, MS students and regularly holds meetings, conferences and gatherings to discuss projects and research.

Real world e-science use-cases

Annette Strauch

This document summarizes two real-world use cases for digital preservation: embedded systems development and financial engineering. For the embedded systems case, the document describes research on developing an autonomous underwater vehicle and identifies relevant data formats, software tools, and hardware. For the financial engineering case, it outlines research calibrating an inflation model using treasury bond data from sources like Bloomberg and analyzing it in MATLAB. Both cases could benefit from emulation to help preserve workflows and access data over the long term.

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The document discusses the AirBox system, an IoT participatory sensing project for monitoring PM2.5 levels. AirBox uses affordable sensors in citizens' homes to create a wider monitoring network compared to traditional expensive stationary monitors. The project aims to enable grassroots environmental monitoring, empower citizens with open-source tools, and engage communities around open data and discussion of air quality issues. Over 4,000 AirBox devices have been deployed across 30 countries to provide finer-grained real-time air pollution data.

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2017/11/9 PNC meeting With emerging concerns of air pollution and recent advances in Internet of Things (IoT) technology, air quality monitoring has become one of the most important IoT applications in many countries and major cities. One of the most successful systems of this kind is the AirBox project, which is based in Taiwan and operated in a grassroot fashion, and it has deployment more than 2,500 nodes in 30 countries in just one year. The system is also notable for its extremely high deployment density and data frequency, as most of the participating nodes are located in Taiwan and with a high sample rate. In this talk, we present the development of this AirBox project in detail, and we demonstrate several data analysis work that we have carried out for data visualization, anomaly detection, data forecast, and other advanced data services. We also discuss challenges and opportunities for making sense of Internet of Things data based on the lessons learned from the AirBox project.

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