PPT-Readability Metrics for Network Visualization

Cody Dunne and Ben Shneiderman HumanComputer Interaction Lab amp Department of Computer Science University of Maryland Contact cdunnecsumdedu 26 th Annual HumanComputer Interaction Lab Symposium. Readability Scores What is a readability score? A readability score is the grade level you need to have completed to be able to read the text. Pros and Cons of Readability Formulas Are text-based . Datang. 欢. 迎. Vaazhga. Welcome. Professor David Wray. University of Warwick,. United Kingdom. Dr Dahlia . Janan. Universiti. . Pendidikan. Sultan . Idris. , Malaysia. Many thanks for coming to today’s meeting. research . on readability in Malay Language . Dr. Dahlia . Janan. Research title:. Matching text to reader: exploring a new model of readability for Malay language . texts. Funded by: . Research Acculturation Grants (RAGS). What is Readability?. A characteristic of text documents... “the sum total of all those elements within a given piece of printed material that affect the success . of a . group of readers have with it. . with Fry’s Readability Graph… What numbers do I use? 1. Randomly select three 100-word segments of 2. Count the number of syllables in each 100-word block and calculate the average. 3. C Calculating the readability of a text. You have been asked to analyze a piece of writing, considering (among other things) its readability. How can you calculate the ease with which it can be read?. Prepared for SSAC by. Lynne Grewe. California State University East . Bay. lynne.grewe@csueastbay.edu. Presentation by . Funda. . Erdin. Disaster Recovery. Issues. Disaster Incident Definition/Protocol. Categories of Data representing Incident. NetViz. Nirvana: Evaluating Social Network Analysis with . NodeXL. 1. Motivation & Goals for Study . NodeXL. evaluation. NetViz. Nirvana & Readability Metrics. Research Methods. Samples of Student Work. System Flow Aggregates. Thienne . Johnson. 1,2. . and Loukas . Lazos. 1. 1. Department . of Electrical and Computer . Engineering. 2. Department . of . Computer Science. University . of . Arizona. 1. bnoble@umich.edu. Campus heavily invested in shared . cyberinfrastructure. Nyx: condo-model HPC cluster, 4K nodes. Flux: 8K nodes, most “rented” by core-month, growth expected. UM3DLab: immersive visualization. GLIF 2015, Prague. Andrew Lee. leea@indiana.edu. 1. Open. , privacy-aware, network measurement, analysis, and visualization service . Designed to address the needs of today’s international networks. metrics. Group level. Density. Components. Isolates. Cliques. Centralization. Degree. Closeness. Betweenness. Factions. Core-periphery. Node level. Centrality. Degree. Indegree. Outdegree. Closeness. : . UNDERSTANDING . OF DEEP NEURAL NETWORKS. By,. Pranav . Murthy,. Masters(CpE). CONVOLUTIONAL NEURAL NETWORK. A convolutional neural network (CNN, or ConvNet) is a type of feed-forward artificial neural network.. Transfer Metrics . to . Optimize Transfer Efficiency . and . Experiment Workflows. Shawn . McKee, Marian . Babik. for the . WLCG Network and Transfer Metrics Working Group. CHEP 2015. , Okinawa, Japan.