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DESIGN AND ANALYSIS OF FACTORIAL-EXPERIMENTS VIA interactive computing IN APL

TECHNOMETRICS 1976年第2期18卷 135-150页

作者： MARGOLIN, BH YALE UNIV DEPT STATISTICSNEW HAVENCT 06520

This paper discusses the use of interactive computing in APL for the design and analysis of an ad hoc experiment. As an example, a project involving a 16 × 4 × 2 experiment in 8 blocks of size 8 is reviewed. The desirability of utilizing single-degreeof-freedom pseudo-factors in both the design and analysis phases of such experiments is exhibited, and the detection of and compensation for heterogeneity of variance and outliers are discussed.

关键词： APL Blocking Fractional Factorial Design Graphical Analysis Heterogeneity of Variance interactive computing Likelihood Ratio Test Outliers Pseudo-factors

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User Real-Time Interest Prediction Based on Collaborative Filtering and interactive computing for Academic Recommendation

User Real-Time Interest Prediction Based on Collaborative Fi...

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8th International Conference on Intelligent computing (ICIC)

作者： Yu, Jie Zhao, Haihong Liu, Fangfang Shanghai Univ Sch Comp Engn & Sci Shanghai 200072 Peoples R China

ISBN: (纸本)9783642315756;9783642315763

With rapid development of Internet, information and resource of Web academic database is great and explosive growth, so it is difficult to quickly and accurately obtain information which meets individual user's needs. Web personalized services can effectively solve the problem of information overload problem and alleviate user's cognitive burden. How to predict user interest is a key issue in Web personalized services. First, this paper proposes concepts of user knowledge unit and user knowledge flow that represents user short-term interest and long-term interest respectively. Second, existing methods have some defects which can't be sensitive to perceive user interest change and accurately predict user real-time interest;we put forward Collaborative Time Weight (CTW) and Collaborative Relation Weight (CRW) to solve those problems. Meanwhile the prediction algorithm for user real-time interest is proposed based on collaborative filtering and interactive computing. Finally, experimental results demonstrate that our method can capture user real-time interests accurately and alleviate the user's cognitive burden effectively.

关键词： Web personalization services User knowledge flow interactive computing Collaborative filtering User interest prediction

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Runbox: Serverless interactive computing Platform 19

Runbox: Serverless Interactive Computing Platform

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12th ACM International Conference on Systems and Storage (SYSTOR)

作者： Glikson, Alex Nie, Shichao Breitgand, David Carnegie Mellon Univ Pittsburgh PA 15213 USA IBM Res Haifa Haifa Israel

ISBN: (纸本)9781450367493

Serverless computing revolutionizes cloud software by eliminating the need to manage the underlying infrastructure, while providing efficient scaling, performance and security isolation as well as usage metering. The goal of Runboxes is to bring the benefits of serverless computing to interactive computing, involving ad-hoc, stateful, resource-intensive tasks, whose resource demand is directly affected by the observed user behavior. We evaluate Runboxes in the context of cost-efficient hosting of sandbox programming environments for Computer Science students.

关键词： Serverless Runbox Kubernetes interactive computing

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Learning Jupyter 5: Explore interactive computing using Python, Java, JavaScript, R, Julia, and JupyterLab, 2nd Edition 2

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2018年

作者： Dan Toomey

ISBN: (数字)9781789137446

ISBN: (纸本)9781789137408

Create and share livecode, equations, visualizations, and explanatory text, in both a single document and a web browser with Jupyter Key Features • Learn how to use Jupyter 5.x features such as cell tagging and attractive table styles • Leverage big data tools and datasets with different Python packages • Explore multiple-user Jupyter Notebook servers Book Description The Jupyter Notebook allows you to create and share documents that contain live code, equations, visualizations, and explanatory text. The Jupyter Notebook system is extensively used in domains such as data cleaning and transformation, numerical simulation, statistical modeling, and machine learning. Learning Jupyter 5 will help you get to grips with interactive computing using real-world examples. The book starts with a detailed overview of the Jupyter Notebook system and its installation in different environments. Next, you will learn to integrate the Jupyter system with different programming languages such as R, Python, Java, JavaScript, and Julia, and explore various versions and packages that are compatible with the Notebook system. Moving ahead, you will master interactive widgets and namespaces and work with Jupyter in a multi-user mode. By the end of this book, you will have used Jupyter with a big dataset and be able to apply all the functionalities you’ve explored throughout the book. You will also have learned all about the Jupyter Notebook and be able to start performing data transformation, numerical simulation, and data visualization. What you will learn • Install and run the Jupyter Notebook system on your machine • Implement programming languages such as R, Python, Julia, and JavaScript with the Jupyter Notebook • Use interactive widgets to manipulate and visualize data in real time • Start sharing your Notebook with colleagues • Invite your colleagues to work with you on the same Notebook • Organize your Notebook using Jupyter namespaces • Access big data in Jupyter for dealing

关键词： IPython IPython Notebook Jupyter Jupyter Notebook interactive computing interactive computing Web Application Jupyter installation Jupyter configuration Jupyter visualization

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interactive information systems: Toward perception based computing

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THEORETICAL COMPUTER SCIENCE 2012年 454卷 240-260页

作者： Skowron, Andrzej Wasilewski, Piotr Warsaw Univ Inst Informat PL-02097 Warsaw Poland Warsaw Univ Inst Math PL-02097 Warsaw Poland

We discuss the role of interactions in the modeling of perception processes. interactive information systems, introduced in this paper, play the important role in this modeling. Moreover, the proposed approach opens a new research direction in rough set theory. In this approach, partial information about the environment used for the approximation of concepts is changing dynamically in a network of interacting information systems contrary to static information systems typically used in rough set theory so far. In particular, we illustrate the use of such information systems for representation of actions or plans, their (changing in time) pre and post conditions. These information systems create a starting point for perception modeling, i.e., modeling of the process of understanding of sensory measurements. We also propose interactive grammars as a tool for modeling interactive computations in perception based computing. (C) 2012 Elsevier B.V. All rights reserved.

关键词： interactive computing interactive information systems interactive tables Rough sets Granular computing Wisdom technology

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interactive and immersive devices with perceptual computing technologies

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MOLECULAR CRYSTALS AND LIQUID CRYSTALS 2017年第1期647卷 329-340页

作者： Bhowmik, Achintya K. Intel Corp Santa Clara CA 95051 USA

In the recent years, we are increasingly endowing devices and machines with the abilities to sense, understand, and interact with us and the physical world, aided by the rapid advances in natural sensing and perceptual computing technologies. In this article, we describe a new class of intelligent systems and immersive applications based on real-time 3D sensing, including interactive computing devices, autonomous robots and drones, as well as augmented and virtual reality systems, blurring the border between the real and the virtual words.

关键词： 3D sensing perceptual computing autonomous robots and drones augmented and virtual reality interactive computing

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JetLag: An interactive, Asynchronous Array computing Environment 20

JetLag: An Interactive, Asynchronous Array Computing Environ...

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Practice and Experience in Advanced Research computing (PEARC)

作者： Brandt, Steven R. Bigelow, Alex Sakin, Sayef Azad Williams, Katy Isaacs, Katherine E. Huck, Kevin Tohid, R. Wagle, Bibek Shirzad, Shahrzad Kaiser, Hartmut Louisiana State Univ Baton Rouge LA 70803 USA Univ Arizona Tucson AZ 85721 USA Univ Oregon Eugene OR 97403 USA

ISBN: (纸本)9781450366892

We describe an interactive computing environment called JetLag. JetLag implements the following features of Phylanx project: (1) Phylanx, a Python-based asynchronous array computing toolkit;(2) the APEX performance measurement library;(3) a performance visualization framework called Traveler;(4) the Tapis/Agave Science as a Service middleware;and (6) a container infrastructure that includes Docker-based Jupyter notebook for the client and a singularity image for the server. The running system starts with a user performing array computations on their workstation or laptop. If, at some point, the calculation the user is performing becomes sufficiently intensive or numerous, it can be packaged and sent to another machine where it will run (through the batch queue system if there is one), produce a result, and have that result sent back to the user's local interface. Whether the calculation is local or remote, the user will be able to use APEX and Traveler to diagnose and fix performance related problems. The JetLag system is suitable for a variety of array computational tasks, including machine learning and exploratory data analysis.

关键词： asynchronous array research environment interactive computing performance tuning performance visualization cloud computing

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Jupyter Cookbook 1

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丛书名： []

2018年

作者： Dan Toomey

ISBN: (数字)9781788839747

ISBN: (纸本)9781788839440

Leverage the power of the popular Jupyter notebooks to simplify your data science tasks without any hassle Key Features Create and share interactive documents with live code, text and visualizations Integrate popular programming languages such as Python, R, Julia, Scala with Jupyter Develop your widgets and interactive dashboards with these innovative recipes Book Description Jupyter has garnered a strong interest in the data science community of late, as it makes common data processing and analysis tasks much simpler. This book is for data science professionals who want to master various tasks related to Jupyter to create efficient, easy-to-share, scientific applications. The book starts with recipes on installing and running the Jupyter Notebook system on various platforms and configuring the various packages that can be used with it. You will then see how you can implement different programming languages and frameworks, such as Python, R, Julia, JavaScript, Scala, and Spark on your Jupyter Notebook. This book contains intuitive recipes on building interactive widgets to manipulate and visualize data in real time, sharing your code, creating a multi-user environment, and organizing your notebook. You will then get hands-on experience with Jupyter Labs, microservices, and deploying them on the web. By the end of this book, you will have taken your knowledge of Jupyter to the next level to perform all key tasks associated with it. What you will learn Install Jupyter and configure engines for Python, R, Scala and more Access and retrieve data on Jupyter Notebooks Create interactive visualizations and dashboards for different scenarios Convert and share your dynamic codes using HTML, JavaScript, Docker, and more Create custom user data interactions using various Jupyter widgets Manage user authentication and file permissions Interact with Big Data to perform numerical computing and statistical modeling Get familiar with Jupyter's next-gen user interface - JupyterLab W

关键词： Jupyter Jupyter Notebook Jupyter Python interactive computing interactive computing Web Application Jupyter installation Jupyter configuration Jupyter R Jupyter visualization

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STATISTICAL computing - HISTORY AND TRENDS

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AMERICAN STATISTICIAN 1980年第4期34卷 238-243页

作者： CHAMBERS, JM Statistics Data Analysis Research Department Bell Laboratories Murray Hill NJ 07974 United States

The evolution of computers is currently in a period of rapid change, stimulated by radically cheaper and smaller devices for processing and memory. These changes are certain to provide major opportunities and challenges for the use of computers in statistics. This article looks at history and current trends, in both general computing and statistical computing, with the goal of identifying key features and requirements for the near future. A discussion of the S language developed at Bell Laboratories illustrates some program design principles that can make future work on statistical programs more effective and more valuable.

关键词： Statistical systems interactive computing Data analysis

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TOOLS - AN ENVIRONMENT FOR TIME-SHARED computing AND PROGRAMMING

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SOFTWARE-PRACTICE & EXPERIENCE 1983年第10期13卷 873-892页

作者： ELLIS, JR MISHKIN, N VANLEUNEN, MC WOOD, SR Department of Computer Science Yale University New Haven Connecticut 06520 U.S.A.

The Yale Tools environment provides integrated modern computing facilities to a mixed group of programmers and users on time-shared machines. Hundreds of people use the environment every day for systems and applications programming, word-processing and university business. The success of the environment is in its integration and engineering detail. Building such a system without a high-level language and a run-time library would have been impossible.

关键词： Programming environments Time-sharing interactive computing Software tools

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