text:
/2047196.2047277},
abstract = {We introduce FingerFlux, an output technique to generate near-surface haptic feedback on interactive tabletops. Our system combines electromagnetic actuation with permanent magnets attached to the user's hand. FingerFlux lets users feel the interface before touching, and can create both attracting and repelling forces. This enables applications such as reducing drifting, adding physical constraints to virtual controls, and guiding the user without visual output. We show that users can feel vibration patterns up to 35 mm above our table, and that FingerFlux can significantly reduce drifting when operating on-screen buttons without looking.},
booktitle = {Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology},
pages = {615–620},
numpages = {6},
keywords = {haptic feedback, actuation, magnets, interactive tabletops},
location = {Santa Barbara, California, USA},
series = {UIST '11}
}

@inproceedings{10.1145/2047196.2047278,
author = {Heo, Seongkook and Lee, Geehyuk},
title = {Force Gestures: Augmenting Touch Screen Gestures with Normal and Tangential Forces},
year = {2011},
isbn = {9781450307161},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2047196.2047278},
doi = {10.1145/2047196.2047278},
abstract = {Force gestures are touch screen gestures augmented by the normal and tangential forces on the screen. In order to study the feasibility of the force gestures on a mobile touch screen, we implemented a prototype touch screen device that can sense the normal and tangential forces of a touch gesture on the screen. We also designed two example applications, a web browser and an e-book reader, that utilize the force gestures for their primary actions. We conducted a user study with the prototype and the applications to study the characteristics of the force gestures and the effectiveness of their mapping to the primary actions. In the user study we could also discover interesting usability issues and collect useful user feedback about the force gestures and their mapping to GUI actions.},
booktitle = {Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology},
pages = {621–626},
numpages = {6},
keywords = {finger properties, touch input, force input},
location = {Santa Barbara, California, USA},
series = {UIST '11}
}

@inproceedings{10.1145/2047196.2047279,
author = {Harrison, Chris and Schwarz, Julia and Hudson, Scott E.},
title = {TapSense: Enhancing Finger Interaction on Touch Surfaces},
year = {2011},
isbn = {9781450307161},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2047196.2047279},
doi = {10.1145/2047196.2047279},
abstract = {We present TapSense, an enhancement to touch interaction that allows conventional surfaces to identify the type of object being used for input. This is achieved by segmenting and classifying sounds resulting from an object's impact. For example, the diverse anatomy of a human finger allows different parts to be recognized including the tip, pad, nail and knuckle - without having to instrument the user. This opens several new and powerful interaction opportunities for touch input, especially in mobile devices, where input is extremely constrained. Our system can also identify different sets of passive tools. We conclude with a comprehensive investigation of classification accuracy and training implications. Results show our proof-of-concept system can support sets with four input types at around 95% accuracy. Small, but useful input sets of two (e.g., pen and finger discrimination) can operate in excess of 99% accuracy.},
booktitle = {Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology},
pages = {627–636},
numpages = {10},
keywords = {collaborative, pens, tools, touchscreen, acoustic classification, input, multi-user, tangibles, finger, interactive surfaces, stylus, tabletop computing},
location = {Santa Barbara, California, USA},
series = {UIST '11}
}

license_type: permissive

extension: bib


text:
/* src/content.css */

/* @import '~antd/dist/antd.css'; */

.my-extension {
    padding: 20px;
}

.my-extension h1 {
    color: #000;
}

#extensionlayout {
    display: flex;
    height: 100vh;
    flex-flow: column nowrap;
}

#extensionheader {
    line-height: 42px;
    height: 44px;
    margin: 0px;
    font-size: 18px;
    padding: 0px;
    color: #eee;
    overflow: hidden;
}

#extensioncontent {
    padding: 15px 10px 10px 10px;
    flex: 2 auto;
    scrollbar-width: thin;
    overflow-y: scroll;
    /* Permalink - use to edit and share this gradient: https://colorzilla.com/gradient-editor/#e1ffff+0,e1ffff+7,e1ffff+12,fdffff+12,e6f8fd+30,c8eefb+54,bee4f8+75,b1d8f5+100;Blue+Pipe+%232 */
    background: rgb(225, 255, 255);
    /* Old browsers */
    background: -moz-linear-gradient(-45deg, rgba(225, 255, 255, 1) 0%, rgba(225, 255, 255, 1) 7%, rgba(225, 255, 255, 1) 12%, rgba(253, 255, 255, 1) 12%, rgba(230, 248, 253, 1) 30%, rgba(200, 238, 251, 1) 54%, rgba(190, 228, 248, 1) 75%, rgba(177, 216, 245, 1) 100%);
    /* FF3.6-15 */
    background: -webkit-linear-gradient(-45deg, rgba(225, 255, 255, 1) 0%, rgba(225, 255, 255, 1) 7%, rgba(225, 255, 255, 1) 12%, rgba(253, 255, 255, 1) 12%, rgba(230, 248, 253, 1) 30%, rgba(200, 238, 251, 1) 54%, rgba(190, 228, 248, 1) 75%, rgba(177, 216, 245, 1) 100%);
    /* Chrome10-25,Safari5.1-6 */
    background: linear-gradient(135deg, rgba(225, 255, 255, 1) 0%, rgba(225, 255, 255, 1) 7%, rgba(225, 255, 255, 1) 12%, rgba(253, 255, 255, 1) 12%, rgba(230, 248, 253, 1) 30%, rgba(200, 238, 251, 1) 54%, rgba(190, 228, 248, 1) 75%, rgba(177, 216, 245, 1) 100%);
    /* W3C, IE10+, FF16+, Chrome26+, Opera12+, Safari7+ */
    filter: progid:DXImageTransform.Microsoft.gradient( startColorstr='#e1ffff', endColorstr='#b1d8f5', GradientType=1);
    /* IE6-9 fallback on horizontal gradient */
}

#extensioncontent::-webkit-scrollbar {
    width: 4px;
    background-color: #333;
}

#extensioncontent::-webkit-scrollbar-thumb {
    background-color: #333;
}

#extensioncontent::-webkit-scrollbar-track {
    -webkit-box-shadow: inset 0 0 4px rgba(0, 0, 0, 0.3);
    box-shadow: inset 0 0 4px rgba(0, 0, 0, 0.3);
    background-color: #F5F5F5;
}

#extensionfooter {
    height: 20px;
    background: #333;
    padding: 0 10px 0 10px;
    color: #eee;
}

.ant-timeline-item-head {
    background: rgba(0, 0, 0, 0)
}

#pagetitle {
    font-weight: bold;
    padding: 0px 10px;
}

.menubutton {
    padding: 10px;
    font-size: 20px;
    transition: all 0.4s ease-in-out;
}

.menubutton:hover {
    background: rgba(255, 255, 255, 0.2);
}

.content-card {
    perspective: 200px;
}

.content-card-enter {
    opacity: 0.01;
    transform: rotateX(-90deg);
    max-height: 0;
}

.content-card-enter.content-card-enter-active {
    opacity: 1;
    transform: none;
    max-height: 150px;
    transition: all 0.4s cubic-bezier(.36, -0.64, .34, 1.76) , max-height 0.15s ease-in;
}

.content-card-exit {
    opacity: 1;
    transform: none;
    max-height: 150px;
}

.content-card-exit.content-card-exit-active {
    opacity: 0.01;
    max-height: 0;
    transform: rotateX(-90deg);
    transition: all 0.3s cubic-bezier(.36, -0.64, .34, 1.76);
}

.ant-drawer-content-wrapper {
    position: absolute !important;
}
.ant-drawer-left{
    transition: width 0s ease 0.3s !important;
}
.ant-drawer-left.ant-drawer-open{
    transition: none !important;
}

license: MIT

repository: miuzel/FairyNote

repo_name: miuzel/FairyNote

branch_name: refs/heads/master

gha_license_id: MIT

gha_language: JavaScript

extension: css


text:
DESCRIPTION = "A regular expression library"
HOMEPAGE = "https://github.com/google/re2/"
LICENSE = "BSD-3-Clause"
LIC_FILES_CHKSUM = "file://LICENSE;md5=3b5c31eb512bdf3cb11ffd5713963760"

SRCREV = "166dbbeb3b0ab7e733b278e8f42a84f6882b8a25"

SRC_URI = "git://github.com/google/re2.git;branch=master;protocol=https"

S = "${WORKDIR}/git"

inherit cmake

EXTRA_OECMAKE += " \
	-DBUILD_SHARED_LIBS=ON \
	-DRE2_BUILD_TESTING=OFF \
"

# Don't include so files in dev package
FILES_${PN} = "${libdir}"
FILES_${PN}-dev = "${includedir} ${libdir}/cmake"

BBCLASSEXTEND = "native nativesdk"

repository: ni/meta-openembedded

repo_name: ni/meta-openembedded

branch_name: refs/heads/nilrt/master/hardknott

gha_language: BitBake

extension: bb


text:
@article{Nyhsen2011,
 abstract = {OBJECTIVES: To evaluate radiology teaching offered to junior doctors on general medical/surgical rotations, their preferences regarding teaching methods and self-directed learning tools, and their suggestions for improvement. METHODS: An online questionnaire involving all foundation programme doctors at Sunderland Royal Hospital was carried out. Quantitative analysis of preferences and free text feedback were used. RESULTS: There was a response rate of 88/90 doctors (98%). The radiology teaching received was mostly informal. Most junior doctors felt that their medical school radiology teaching had been inadequate. The preferred teaching techniques were interactive case-based and system-based discussions. Textbooks and journal articles were not as popular as self-directed learning tools. Online learning material was used quite frequently, with general web content being more popular. Eighty-seven percent cited their motivation for studying radiology as "to become a better doctor". More guidance from radiologists was desired, particularly regarding the choice of examinations and discussion of cases. Twenty-two percent of doctors were considering radiology as a career. CONCLUSIONS: Interactive elements in radiology teaching are important. Online electronic teaching modules can be integrated into the teaching curriculum, but they must be of high quality to be acceptable and face-to-face interaction is still important. Junior doctors would like more guidance from radiologists.},
 author = {Nyhsen, Christiane M and Lawson, Cathy and Higginson, Jim},
 doi = {10.1007/s13244-010-0052-5},
 file = {:home/viridis/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Nyhsen, Lawson, Higginson - 2011 - Radiology teaching for junior doctors their expectations, preferences and suggestions for improvement.pdf:pdf},
 issn = {1869-4101},
 journal = {Insights into imaging},
 keywords = {hospitals,teaching,teaching methods},
 month = {jun},
 number = {3},
 pages = {261--266},
 pmid = {22347952},
 title = {Radiology teaching for junior doctors: their expectations, preferences and suggestions for improvement.},
 url = {http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=3259358&tool=pmcentrez&rendertype=abstract},
 volume = {2},
 year = {2011}
}

repository: jimhigginson/academic-kickstart

repo_name: jimhigginson/academic-kickstart


text:
@font-face {font-family: "tf";
  src: url('iconfont.eot?t=1498631331269'); /* IE9*/
  src: url('iconfont.eot?t=1498631331269#iefix') format('embedded-opentype'), /* IE6-IE8 */
  url('iconfont.woff?t=1498631331269') format('woff'), /* chrome, firefox */
  url('iconfont.ttf?t=1498631331269') format('truetype'), /* chrome, firefox, opera, Safari, Android, iOS 4.2+*/
  url('iconfont.svg?t=1498631331269#tf') format('svg'); /* iOS 4.1- */
}

.tf {
  font-family:"tf" !important;
  font-size:16px;
  font-style:normal;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
}

.tf-search-plus:before { content: "\e699"; }

.tf-qrcode:before { content: "\e601"; }

.tf-voice-fill:before { content: "\e66a"; }

.tf-draw-line-measure:before { content: "\e630"; }

.tf-search-minus:before { content: "\e63d"; }

.tf-clear-fill:before { content: "\e6a6"; }

.tf-voice-forbid-fill:before { content: "\e6db"; }

.tf-draw-pencil:before { content: "\e70a"; }

.tf-minus:before { content: "\e60b"; }

.tf-map-point:before { content: "\e7b9"; }

.tf-dolphin:before { content: "\e603"; }

.tf-info-circle:before { content: "\e629"; }

.tf-draw-rect:before { content: "\e604"; }

.tf-draw-circle:before { content: "\e606"; }

.tf-draw-polygon:before { content: "\e607"; }

.tf-mouse-pointer:before { content: "\e65c"; }

.tf-search:before { content: "\e65f"; }

.tf-setting:before { content: "\e602"; }

.tf-move-lr:before { content: "\e631"; }

.tf-circle-point:before { content: "\e61c"; }

.tf-draw-line:before { content: "\e61d"; }

.tf-screen:before { content: "\e6f5"; }

.tf-draw-curve:before { content: "\e625"; }

.tf-plus:before { content: "\e7d3"; }

repository: mocheer/react-view

repo_name: mocheer/react-view


text:
SECTION = "console/utils"
DESCRIPTION = "Orpheus is a light-weight text mode menu- and \
window-driven audio player application for CDs and files in \
MP3 and Vorbis OGG format."
DEPENDS = "ncurses"
LICENSE = "GPL"
PR = "r1"

SRC_URI = "http://thekonst.net/download/orpheus-${PV}.tar.bz2 \
	   file://m4.patch"
S = "${WORKDIR}/orpheus-${PV}"

inherit autotools

acpaths = "-I ${S}/m4"

SRC_URI[md5sum] = "9613e6be14ba7a0ca5d42a88e6ed3516"
SRC_URI[sha256sum] = "45145ea27333be1552624065297b4d9d76feadf2a4a89fdf0ddafc830ced3a98"

repository: overo/overo-oe-natty

repo_name: overo/overo-oe-natty

branch_name: HEAD


text:
@article{Aksamit-2018-Scale,
    title = "Scale Interactions in Turbulence for Mountain Blowing Snow",
    author = "Aksamit, N. O.  and
      Pomeroy, J. W.",
    journal = "Journal of Hydrometeorology, Volume 19, Issue 2",
    volume = "19",
    number = "2",
    year = "2018",
    publisher = "American Meteorological Society",
    url = "https://aclanthology.org/G18-6001",
    doi = "10.1175/jhm-d-17-0179.1",
    pages = "305--320",
}

repository: gwf-uwaterloo/gwf-publications

repo_name: gwf-uwaterloo/gwf-publications

branch_name: refs/heads/main

gha_language: Python


text:
@InProceedings{du14,
  supplementary = {Supplementary:du14-supp.zip},
  title = {Influence Function Learning in Information Diffusion Networks},
  author = {Nan Du and Yingyu Liang and Maria Balcan and Le Song},
  pages = {2016-2024},
  abstract = {Can we learn the influence of a set of people in a social network from cascades of information diffusion? This question is often addressed by a two-stage approach: first learn a diffusion model, and then calculate the influence based on the learned model. Thus, the success of this approach relies heavily on the correctness of the diffusion model which is hard to verify for real world data. In this paper, we exploit the insight that the influence functions in many diffusion models are coverage functions, and propose a novel parameterization of such functions using a convex combination of random basis functions. Moreover, we propose an efficient maximum likelihood based algorithm to learn such functions directly from cascade data, and hence bypass the need to specify a particular diffusion model in advance. We provide both theoretical and empirical analysis for our approach, showing that the proposed approach can provably learn the influence function with low sample complexity, be robust to the unknown diffusion models, and significantly outperform existing approaches in both synthetic and real world data.},
  section = {cycle-2},
}

repository: mreid/papersite

repo_name: mreid/papersite

gha_language: TeX


text:
include(CMakeFindDependencyMacro)
set(CMAKE_PREFIX_PATH ${CMAKE_CURRENT_LIST_DIR})
find_dependency(PocoFoundation)
find_dependency(PocoNet)
include("${CMAKE_CURRENT_LIST_DIR}/PocoMongoDBTargets.cmake")

repository: dtylman/ion

repo_name: dtylman/ion

extension: cmake