Maps and Google Fusion Tables Version 3 — November 2013
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Google Docs Accessibility (Pdf)
Google Docs Accessibility (A11y) Building Accessible Google Docs • Heading Styles • Images • Table of Contents • Captioning • Columns and Lists • Tables A11y • Tab Stops • Color Contrast • Paragraph Spacing • Headers and Footers • Meaningful Link Text • Accessibility Checker What is Assistive Technology? Assistive Technology (AT) are “products, equipment, and systems that enhance learning, working, and daily living for persons with disabilities.” Magnification Speech Screen Readers Software Recognition Trackball Mouse Keyboard Zoom Text Braille Computer Keyboard Captions/Subtitles Captioned Telephone Video Relay Services Captioning Videos Per federal and state law, and CSU policy, instructional media (e.g., videos, captured lectures, recorded presentations) must have captions. This includes instructional media used in classrooms, posted on websites or shared in Canvas. • All students who are enrolled in a course must be able to access the content in the course. • Faculty: Funding is available to help faculty generate captions and transcripts for instructional media. Materials should be submitted at least six weeks in advance of their use in instruction. • Staff: For CSUN staff who do not provide classroom material, there is a cost through chargeback. For information on the chargeback, email [email protected]. csun.edu/captioning What are Screen Readers Screen readers are a form of assistive technology (AT) software that enables access to a computer, and all the things a computer does, by attempting to identify and interpret what is being displayed on the computer screen using text-to-speech. Screen readers can only access and process live text (fully editable or selectable text). • Provides access to someone who is visually impaired, mobility or has a learning disability to access text on the screen. -
Idaho Highway Wildlife Mortality
Idaho Highway Wildlife Mortality A. James Frankman Abstract—Idaho wildlife mortalities on highways and roads is tracked by the Idaho Fish and Game and the data is made available to the general public through an API called IFWIS Core. While the data supplied does offer species information and geographic coordinates, it can be difficult to organize and understand. This paper will attempt to organize and present this data in visual form using Google Maps and Visualizations APIs to show facets of wildlife mortality in Idaho by density of occurance, time of year, and species variety Index Terms—Information Visualization, Idaho Fish and Game, IFWIS Core, road kill, wildlife mortality, Google Visualization API. 1 INTRODUCTION Amongst the rural communities throughout the United States, the only shows 250 of the latest observations, the density of markers on attrition of wildlife by highway collision is a common occurrence. the map make it difficult to distinguish individual incidents. In an effort to better track and understand wildlife collisions occurrences, the Idaho Fish and Game tracks highway collisions that have occurred since 2001. This data can be useful and relevant to several areas of study. First, understanding how and where collisions occur can help prevent traffic accidents. According to the National Highway Traffic Administration 4% of all traffic accidents in the United States are collisions with wildlife[1]. The collisions with wildlife on U.S. roads and highways represent a significant safety concern to motorists. Besides the risks posed to motorists, the affect on wildlife populations is also significant. America’s wildlife is a natural resource, and highway collisions have a negative impact on wildlife populations. -
Profiles Research Networking Software Installation Guide
Profiles Research Networking Software Installation Guide Documentation Version : July 25, 2014 Software Version : ProfilesRNS_2.1.0 Table of Contents Introduction ..................................................................................................................... 2 Hardware and Operating System Requirements ............................................................. 3 Download Options ........................................................................................................... 4 Installing the Database .................................................................................................... 5 Loading Person Data....................................................................................................... 8 Loading Person Data: Part 1 – Importing SSIS Packages into SQL Server msdb Database ..................................................................................................................... 8 Loading Person Data: Part 2 – Importing Demographic Data .................................... 10 Loading Person Data: Part 3 – Geocoding ................................................................ 15 Loading Person Data: Part 4 – Obtaining Publications .............................................. 16 Loading Person Data: Part 5 – Convert data to RDF ................................................. 19 Scheduling Database Jobs ............................................................................................ 21 Installing the Code........................................................................................................ -
A Comparison of Natural Language Understanding Platforms for Chatbots in Software Engineering
1 A Comparison of Natural Language Understanding Platforms for Chatbots in Software Engineering Ahmad Abdellatif, Khaled Badran, Diego Elias Costa, and Emad Shihab, Senior Member, IEEE Abstract—Chatbots are envisioned to dramatically change the future of Software Engineering, allowing practitioners to chat and inquire about their software projects and interact with different services using natural language. At the heart of every chatbot is a Natural Language Understanding (NLU) component that enables the chatbot to understand natural language input. Recently, many NLU platforms were provided to serve as an off-the-shelf NLU component for chatbots, however, selecting the best NLU for Software Engineering chatbots remains an open challenge. Therefore, in this paper, we evaluate four of the most commonly used NLUs, namely IBM Watson, Google Dialogflow, Rasa, and Microsoft LUIS to shed light on which NLU should be used in Software Engineering based chatbots. Specifically, we examine the NLUs’ performance in classifying intents, confidence scores stability, and extracting entities. To evaluate the NLUs, we use two datasets that reflect two common tasks performed by Software Engineering practitioners, 1) the task of chatting with the chatbot to ask questions about software repositories 2) the task of asking development questions on Q&A forums (e.g., Stack Overflow). According to our findings, IBM Watson is the best performing NLU when considering the three aspects (intents classification, confidence scores, and entity extraction). However, the results from each individual aspect show that, in intents classification, IBM Watson performs the best with an F1-measure>84%, but in confidence scores, Rasa comes on top with a median confidence score higher than 0.91. -
Connected Cl As Sroom
Data in the Cloud he ability to move from one representation monthly temperatures for cities in the United of data to another is one of the key char- States, South America, and Russia. Tacteristics of expert mathematicians and A quick inspection of the graph makes it evi- scientists. Cloud computing will offer more dent that on average it is always colder in Verk- opportunities to create and display multiple hoyansk than in Washington (and much colder representations of data, making this skill in the winter) and that the seasons in the south- even more important in the future. ern hemisphere are reversed. These patterns would have been much more difficult to discern Multiple Representations in a table of numbers alone. We can represent data in a variety of forms— Even young students can explore multiple rep- graphs, charts, tables of numbers, equations. resentations of the same data with software such Mathematicians, scientists, and engineers often as The Graph Club (Tom Snyder Productions). look for patterns in data. Different representations This application allows students to view two of the same data sometimes make it easier to see linked representations of data simultaneously. As a pattern. For example, the pattern in the table children drag icons to form a picture graph or of numbers below is not immediately evident. enter numbers in a simple table, a corresponding bar graph or pie chart takes shape (see Figure 2). Changing the size of a sector in the circle graph or the height of a bar in a bar chart Table 1. changes the pictogram or vice versa. -
Voice.AI Gateway One-Click Dialogflow Integration Guide
Integration Guide AudioCodes Intuitive Human Communications for Chatbot Services Voice.AI Gateway Google One-Click DialogFlow Integration Integration Guide Contents Table of Contents 1 Introduction ......................................................................................................... 7 1.1 Purpose .................................................................................................................. 7 2 Messages Sent by Voice.AI Gateway ................................................................ 9 2.1 Welcome Message ................................................................................................. 9 3 Messages Sent by Agent .................................................................................. 11 3.1 Basic Activity Syntax ............................................................................................ 11 3.2 hangup activity ..................................................................................................... 11 3.3 Bot Framework Specific Details ............................................................................ 12 3.3.1 Google Dialogflow ....................................................................................................12 Voice Bot Solutions 3 Voice.AI Gateway One-Click Dialogflow List of Tables Table 2-1: Description of Initial Message Sent by Voice.AI Gateway ...................................................... 9 Table 3-1: Properties of JSON Object Activities.....................................................................................11 -
The Ultimate Guide to Google Sheets Everything You Need to Build Powerful Spreadsheet Workflows in Google Sheets
The Ultimate Guide to Google Sheets Everything you need to build powerful spreadsheet workflows in Google Sheets. Zapier © 2016 Zapier Inc. Tweet This Book! Please help Zapier by spreading the word about this book on Twitter! The suggested tweet for this book is: Learn everything you need to become a spreadsheet expert with @zapier’s Ultimate Guide to Google Sheets: http://zpr.io/uBw4 It’s easy enough to list your expenses in a spreadsheet, use =sum(A1:A20) to see how much you spent, and add a graph to compare your expenses. It’s also easy to use a spreadsheet to deeply analyze your numbers, assist in research, and automate your work—but it seems a lot more tricky. Google Sheets, the free spreadsheet companion app to Google Docs, is a great tool to start out with spreadsheets. It’s free, easy to use, comes packed with hundreds of functions and the core tools you need, and lets you share spreadsheets and collaborate on them with others. But where do you start if you’ve never used a spreadsheet—or if you’re a spreadsheet professional, where do you dig in to create advanced workflows and build macros to automate your work? Here’s the guide for you. We’ll take you from beginner to expert, show you how to get started with spreadsheets, create advanced spreadsheet-powered dashboard, use spreadsheets for more than numbers, build powerful macros to automate your work, and more. You’ll also find tutorials on Google Sheets’ unique features that are only possible in an online spreadsheet, like built-in forms and survey tools and add-ons that can pull in research from the web or send emails right from your spreadsheet. -
CHELLY JIN I'm a Designer Synthesizing Data, Technology, and Art to Illustrate Narratives Through Interactive Multimedia and Creative Research
CHELLY JIN I'm a designer synthesizing data, technology, and art to illustrate narratives through interactive multimedia and creative research. I believe in the application of design thinking and methodology to develop collaborative, innovative solutions. Specializing in UI/UX, interaction design, data visualization, VR application, web development, and design research methods with a background in graphic design. [email protected] | www.chellyj.in | 832.205.7183 SKILLS Creative Coding Data Visualization Video Virtual Reality HTML & CSS Tableau Premiere Pro Unity Javascript Google Fusion Tables Cinematography C# Processing Production p5.js Illustration and Image Illustrator Motion and 3D UI / UX Photoshop After Effects Sketch Photography Cinema 4D Maya [email protected] | www.chellyj.in | 832.205.7183 Perf board / Arduino setup fishing line hardware cloth wire lanterns 6 ft 6 ft PROJECT Senior Project Captsone 2018 HYPNAGOGIA TOOLS Hypnagogia is the liminal state between consciousness and dream, a transitional flow that occurs in the mind. Arduino, Processing, Installation This installation and video piece illustrates the hypnagogic duality with lights orchestrated by her conscious- making and hardware circuitry, Video editing in Adobe Premiere ness, using EEG brainwave sensors, as the artist reveals her subconsciousness, by reading aloud the dreams Pro, Book / print design she's written down from the past six years. on Adobe InDesign, [email protected] | www.chellyj.in | 832.205.7183 PROJECT NASA Jet Propulsion Laboratory MERIDIAN Data Visualization Intern Project Telemetry data visualization system for NASA Jet Propulsion Lab's Mars Exploration Rover (MER) team for TOOLS the rover, Opportunity. Meridian visualizes optimal orientations of the rover for the maximum available data Sketch , Adobe Illustrator, Paperprototyping, Javascript transfer over time with decision-making tools. -
Recovering Semantics of Tables on the Web
Recovering Semantics of Tables on the Web Petros Venetis Alon Halevy Jayant Madhavan Marius Pas¸ca Stanford University Google Inc. Google Inc. Google Inc. [email protected] [email protected] [email protected] [email protected] Warren Shen Fei Wu Gengxin Miao Chung Wu Google Inc. Google Inc. UC Santa Barbara Google Inc. [email protected] [email protected] [email protected] [email protected] ABSTRACT away. Furthermore, unlike text documents, where small changes The Web offers a corpus of over 100 million tables [6], but the in the document structure or wording do not correspond to vastly meaning of each table is rarely explicit from the table itself. Header different content, variations in table layout or terminology change rows exist in few cases and even when they do, the attribute names the semantics significantly. In addition to table search, knowing are typically useless. We describe a system that attempts to recover the semantics of tables is also necessary for higher-level operations the semantics of tables by enriching the table with additional anno- such as combining tables via join or union. tations. Our annotations facilitate operations such as searching for In principle, we would like to associate semantics with each table tables and finding related tables. in the corpus, and use the semantics to guide retrieval, ranking and To recover semantics of tables, we leverage a database of class table combination. However, given the scale, breadth and hetero- labels and relationships automatically extracted from the Web. The geneity of the tables on the Web, we cannot rely on hand-coded database of classes and relationships has very wide coverage, but domain knowledge. -
A Simple Generic Attack on Text Captchas
A Simple Generic Attack on Text Captchas Haichang Gao1*, Jeff Yan2*, Fang Cao1, Zhengya Zhang1, Lei Lei1, Mengyun Tang1, Ping Zhang1, Xin Zhou1, Xuqin Wang1 and Jiawei Li1 1. Institute of Software Engineering, Xidian University, Xi’an, Shaanxi, 710071, P.R. China 2. Security Lancaster & School of Computing and Communications, Lancaster University, UK ∗Corresponding authors: [email protected], [email protected] Abstract—Text-based Captchas have been widely deployed attacked many early Captchas deployed on the Internet [19]. across the Internet to defend against undesirable or malicious Yan and El Ahmad broke most visual schemes provided at bot programs. Many attacks have been proposed; these fine prior Captchaservice.org in 2006 [24], published a segmentation art advanced the scientific understanding of Captcha robustness, attack on Captchas deployed by Microsoft and Yahoo! [25] but most of them have a limited applicability. In this paper, in 2008, and broke the Megaupload scheme with a method we report a simple, low-cost but powerful attack that effectively of identifying and merging character components in 2010 [1]. breaks a wide range of text Captchas with distinct design features, including those deployed by Google, Microsoft, Yahoo!, Amazon In 2011, Bursztein et al. showed that 13 Captchas on pop- and other Internet giants. For all the schemes, our attack achieved ular websites were vulnerable to automated attacks, but they a success rate ranging from 5% to 77%, and achieved an achieved zero success on harder schemes such as reCAPTCHA average speed of solving a puzzle in less than 15 seconds on and Google’s own scheme [5]. -
Vendor Contract
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Google-Forms-Table-Input.Pdf
Google Forms Table Input Tutelary Kalle misconjectured that norepinephrine picnicking calligraphy and disherit funnily. Inventible and perpendicular Emmy devilling her secureness overpopulating smokelessly or depolarize uncontrollably, is Josh gnathonic? Acidifiable and mediate Townie sneak slubberingly and single-foot his avowers high-up and bloodthirstily. You can use some code, but use this quantity is extremely useful at url and input table Google Forms Date more Time Robertorecchimurzoit. How god set wallpaper a Google Sheet insert a reliable data represent Data. 25 practical ways to use Google Forms in class school Ditch. To read add any question solve a google form using the plus button and wire change our question new to complex choice exceed The question screen shows Rows OptionsAnswer and Columns TopicQuestion that nothing be added in significant amount of example below shows a three-row otherwise four-column point question. How about insert text table in google forms for matrices qustions. Techniques using Add-ons Formulas Formatting Pivot Tables or Apps Script. Or the filetype operator Google searches a sequence of file formats see the skill in. To rest a SQLish syntax in fuel cell would return results from a such in Sheets. Google Form Responses Spreadsheet Has Blank Rows or No. How to seduce a Dynamic Chart in Google Newco Shift. My next available in input table, it against it with it and adds instrument to start to auto populate a document? The steps required to build a shiny app that mimicks a Google Form. I count a google form complete a multiple type question. Text Field React component Material-UI.