drawing intermediate lines and playback icon
This commit is contained in:
Ruben van de Ven
parent
e25960f2e0
commit
a15195237d
1 changed files with 657 additions and 14 deletions
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canvas.html
655
canvas.html
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@ -80,11 +80,46 @@
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top: 0;
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top: 0;
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}
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}
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div[data-custom-style='Body Text'] p {
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div[data-custom-style='Body Text'] p {
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padding: 1em 0;
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padding: 1em 0;
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margin: 0;
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margin: 0;
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background-color: rgba(255, 255, 255, 0.8);
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background-color: rgba(255, 255, 255, 0.8);
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}
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}
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.anchor{
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position: relative;
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}
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.anchor.active:not(.playing)::before{
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content:'⏵';
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position: absolute;
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width: 40px;
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height: 40px;
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background:gray;
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left: calc(50% - 20px);
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top: calc(50% - 20px);
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vertical-align: middle;
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line-height: 35px;
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border-radius: 5px;
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color:white;
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}
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.anchor.active:not(.playing):hover::before{
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background:black
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}
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.anchor.playing:hover::before{
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content:'⏸︎';
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position: absolute;
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width: 40px;
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height: 40px;
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background:black;
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left: calc(50% - 20px);
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top: calc(50% - 20px);
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vertical-align: middle;
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line-height: 35px;
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border-radius: 5px;
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color:white;
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}
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</style>
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</style>
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<link rel="stylesheet" href="paper.css" />
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<link rel="stylesheet" href="paper.css" />
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<script src="assets/wNumb-1.2.0.min.js"></script>
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<script src="assets/wNumb-1.2.0.min.js"></script>
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@ -116,13 +151,21 @@
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}
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}
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div.controls{display:none !important;}`
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div.controls{display:none !important;}`
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);
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);
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console.log(sheet, sheet.rules)
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function easeInOutQuart(x) {
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function easeInOutQuart(x) {
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return x < 0.5 ? 8 * x * x * x * x : 1 - Math.pow(-2 * x + 2, 4) / 2;
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return x < 0.5 ? 8 * x * x * x * x : 1 - Math.pow(-2 * x + 2, 4) / 2;
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}
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}
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function easeInOutBack(x) {
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const c1 = 1.70158;
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const c2 = c1 * 1.525;
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return x < 0.5
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? (Math.pow(2 * x, 2) * ((c2 + 1) * 2 * x - c2)) / 2
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: (Math.pow(2 * x - 2, 2) * ((c2 + 1) * (x * 2 - 2) + c2) + 2) / 2;
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}
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let scroll_offsets = []
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let scroll_offsets = []
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@ -154,10 +197,11 @@
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paths.forEach((el) => el.style.strokeDasharray = Math.ceil(el.getTotalLength()) + 'px');
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paths.forEach((el) => el.style.strokeDasharray = Math.ceil(el.getTotalLength()) + 'px');
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const lastAnchorEl = anchorEls[anchorEls.length - 1];
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const lastAnchorEl = anchorEls[anchorEls.length - 1];
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console.log(lastAnchorEl)
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for (const anchorEl of anchorEls) {
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for (const anchorEl of anchorEls) {
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anchorEl.addEventListener('click', ev => playerEls[anchorEl.dataset.i].annotator.playPause());
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anchorEl.addEventListener('click', ev => playerEls[anchorEl.dataset.i].annotator.playPause());
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playerEls[anchorEl.dataset.i].annotator.addEventListener('play', ev => anchorEl.classList.add('playing'));
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playerEls[anchorEl.dataset.i].annotator.addEventListener('pause', ev => anchorEl.classList.remove('playing'));
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}
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}
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for (const player of playerEls) {
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for (const player of playerEls) {
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player.shadowRoot.adoptedStyleSheets = [sheet];
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player.shadowRoot.adoptedStyleSheets = [sheet];
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@ -231,6 +275,7 @@
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const t = Math.min(1, Math.max(0, (center_y - prev[0]) / (next[0] - prev[0])))
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const t = Math.min(1, Math.max(0, (center_y - prev[0]) / (next[0] - prev[0])))
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t_ease = easeInOutQuart(t)
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t_ease = easeInOutQuart(t)
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// t_ease = easeInOutBack(t)
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const dx = target_pos[0] - source_pos[0];
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const dx = target_pos[0] - source_pos[0];
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const dy = target_pos[1] - source_pos[1];
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const dy = target_pos[1] - source_pos[1];
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@ -246,7 +291,6 @@
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y = -1 * (source_pos[1] + dy * t_ease) * scale + window.innerHeight / 2;
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y = -1 * (source_pos[1] + dy * t_ease) * scale + window.innerHeight / 2;
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const color = (target_color - source_color) * t_ease + source_color
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const color = (target_color - source_color) * t_ease + source_color
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console.log(sheet)
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// sheet.rules[0].style.setProperty('--override-color', `rgba(${color},${color},${color},0.7)`);
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// sheet.rules[0].style.setProperty('--override-color', `rgba(${color},${color},${color},0.7)`);
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sheet.rules[0].style.setProperty('--disactive-path', `rgba(${color},${color},${color},0.7)`);
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sheet.rules[0].style.setProperty('--disactive-path', `rgba(${color},${color},${color},0.7)`);
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@ -282,7 +326,6 @@
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// calculate whether we're nearing the conlusion, and color accordingly
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// calculate whether we're nearing the conlusion, and color accordingly
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const last = Math.max(...Array.from(anchorEls).map((e) => e.getBoundingClientRect().bottom))
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const last = Math.max(...Array.from(anchorEls).map((e) => e.getBoundingClientRect().bottom))
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console.log(last)
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if (last < 0) {
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if (last < 0) {
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for (const playerEl of playerEls) {
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for (const playerEl of playerEls) {
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playerEl.classList.add('ending')
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playerEl.classList.add('ending')
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@ -307,7 +350,7 @@
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let options = {
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let options = {
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// root: document.querySelector("#scrollArea"), // viewport by default
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// root: document.querySelector("#scrollArea"), // viewport by default
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rootMargin: `${-Math.round(innerHeight / 3)}px`, //"0px",
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rootMargin: `${-Math.floor((window.innerHeight-10) / 2)}px 0px`, //"0px",
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threshold: 0,
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threshold: 0,
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};
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};
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@ -320,8 +363,10 @@
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}
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}
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playerEl = windowEl.children[index];
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playerEl = windowEl.children[index];
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if (entry.isIntersecting) {
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if (entry.isIntersecting) {
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entry.target.classList.add('active');
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playerEl.classList.add('active')
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playerEl.classList.add('active')
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} else {
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} else {
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entry.target.classList.remove('active');
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playerEl.classList.remove('active')
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playerEl.classList.remove('active')
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if (typeof playerEl.annotator.paused !== 'undefined' && !playerEl.annotator.paused) {
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if (typeof playerEl.annotator.paused !== 'undefined' && !playerEl.annotator.paused) {
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console.log('pause', playerEl.annotator, playerEl.annotator.paused)
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console.log('pause', playerEl.annotator, playerEl.annotator.paused)
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@ -335,7 +380,7 @@
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for (const anchorEl of anchorEls) {
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for (const anchorEl of anchorEls) {
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observer.observe(anchorEl)
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observer.observe(anchorEl)
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}
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}
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console.log(anchorEls)
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// console.log(anchorEls)
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// .forEach(el => observer.observe());
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// .forEach(el => observer.observe());
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@ -470,6 +515,8 @@
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</div>
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</div>
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</div>
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</div>
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<section id="part1">
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<section id="part1">
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<p> .... this is a demo to showcase how the chronodiagramming looks like in its interactive form. Please note
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that this demo of the interface is not compatible with mobile devices ...</p>
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<section id="managing-error-from-the-sublime-to-the-risky-algorithm" class="level2">
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<section id="managing-error-from-the-sublime-to-the-risky-algorithm" class="level2">
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<h2>3. Managing error: from the sublime to the risky algorithm</h2>
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<h2>3. Managing error: from the sublime to the risky algorithm</h2>
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<div data-custom-style="Body Text">
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<div data-custom-style="Body Text">
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@ -835,6 +882,602 @@
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visibilization, invisibilization, responsibility and accountability.</p>
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visibilization, invisibilization, responsibility and accountability.</p>
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</div>
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</div>
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</section>
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</section>
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</section>
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<section id="a-diagram-of-research" class="level1">
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<h1>A diagram of research</h1>
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<div data-custom-style="Body Text">
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<p>In this conclusion, we reflect upon a final dimension of the method
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of diagraming in the context of figurations and configurations: its
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potential as an alternative to the conventional research program.</p>
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</div>
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<div data-custom-style="Body Text">
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<p>While writing this text, indeed, the search for a coherent structure
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through which we could map the problems that emerged from analyzing the
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diagrams in a straightforward narrative proved elusive. We considered
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various organizational frameworks, but consistently encountered
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resistance from one or two sections. It became evident that our
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interviews yielded a rhizome of interrelated problems, creating a
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multitude of possible inquiries and overlapping trajectories. Some
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dimensions of these problems are related, but not to every problem.</p>
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</div>
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<div data-custom-style="Body Text">
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<p>If we take for example the understanding of algorithmic security
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vision as practices of error management as a starting point, we see how
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the actors we interviewed have incorporated the societal critique of
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algorithmic bias. This serves as a catalyst for novel strategies aimed
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at mitigating the repercussions of imperfect systems. The societal
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critique has driven the development of synthetic datasets, which promise
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equitable representation across diverse demographic groups. It has also
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been the reason for the reliance on institutionalized benchmarks to
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assess the impartiality of algorithms. Moreover, different
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configurations of the human-in-the-loop emerge, all promised to rectify
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algorithmic fallibility. We see a causal chain there.</p>
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</div>
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<div data-custom-style="Body Text">
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<p>But how does the question of algorithmic error relate to the shift
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from photographic to cinematic vision that algorithmic security vision
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brings about? Certainly, there are reverberations. The relegation of
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stable identity that we outlined, could be seen as a way to mitigate the
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impact of those errors. But it would be a leap to identify these
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questions of error as the central driver for the increased incorporation
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of moving images in algorithmic security vision.</p>
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</div>
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<div data-custom-style="Body Text">
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<p>However, if we take as our starting point the formidable strides in
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computing power and the advancements in camera technologies, we face
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similar problems. These developments make the analysis of movement
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possible while helping to elucidate the advances in the real-time
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analysis that are required to remove the human-in-the-loop, as trialed
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in the Burglary-Free Neighborhood. These developments account for the
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feasibility of the synthetic data generation, a computing-intense
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process which opens a vast horizon of possibilities for developers to
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detect objects or actions. Such an account, however, does not address
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the need for such a synthetic dataset. A focus on the computation of
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movement, however, would highlight how a lack of training data
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necessitates many of the practices described. Synthetic data is
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necessitated by the glaring absence of pre-existing security datasets
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that contain moving bodies. While facial recognition algorithms could be
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trained and operated on quickly repurposed photographic datasets of
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national identity cards or drivers’ license registries, no dataset for
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moving bodies has been available to be repurposed by states or
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corporations. This absence of training data requires programmers to
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stage scenes for the camera. Thus, while one issue contains echoes of
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the other, the network of interrelated problematizations cannot be
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flattened into a single narrative.</p>
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</div>
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<div data-custom-style="Body Text">
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<p>The constraints imposed by the linear structure of an academic
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article certainly necessitate a specific ordering of sections. Yet the
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different research directions we highlight form something else. The
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multiple figurations analyzed here generate fresh tensions when put in
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relation with security and political practices. What appears from the
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diagrams is a network of figurations in various configurations. Instead
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of a research <em>program</em>, our interviews point toward a larger
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research <em>diagram</em> of interrelated questions, which invites us to
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think in terms of pathways through this dynamic and evolving network of
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relations.</p>
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</div>
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</section>
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<section id="references" class="level1">
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<h1>References</h1>
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<div data-custom-style="Bibliography">
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<p>Ajana B (2013) <em>Governing Through Biometrics</em>. London:
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Palgrave Macmillan UK. DOI: <a href="https://doi.org/10.1057/9781137290755"><span
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data-custom-style="Hyperlink">10.1057/9781137290755</span></a>.</p>
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</div>
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<div data-custom-style="Bibliography">
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<p>Amicelle A, Aradau C and Jeandesboz J (2015) Questioning security
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devices: Performativity, resistance, politics. <em>Security
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Dialogue</em> 46(4): 293–306. DOI: <a href="https://doi.org/10.1177/0967010615586964"><span
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data-custom-style="Hyperlink">10.1177/0967010615586964</span></a>.</p>
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</div>
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<div data-custom-style="Bibliography">
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<p>Amoore L (2014) Security and the incalculable. <em>Security
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Dialogue</em> 45(5). SAGE Publications Ltd: 423–439. DOI: <a
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href="https://doi.org/10.1177/0967010614539719"><span
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data-custom-style="Hyperlink">10.1177/0967010614539719</span></a>.</p>
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</div>
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<div data-custom-style="Bibliography">
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<p>Amoore L (2019) Doubt and the algorithm: On the partial accounts of
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machine learning. <em>Theory, Culture & Society</em> 36(6). SAGE
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Publications Ltd: 147–169. DOI: <a href="https://doi.org/10.1177/0263276419851846"><span
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data-custom-style="Hyperlink">10.1177/0263276419851846</span></a>.</p>
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</div>
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<div data-custom-style="Bibliography">
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<p>Amoore L (2021) The deep border. <em>Political Geography</em>.
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Elsevier: 102547.</p>
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</div>
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<div data-custom-style="Bibliography">
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<p>Amoore L and De Goede M (2005) Governance, risk and dataveillance in
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the war on terror. <em>Crime, Law and Social Change</em> 43(2): 149–173.
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DOI: <a href="https://doi.org/10.1007/s10611-005-1717-8"><span
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data-custom-style="Hyperlink">10.1007/s10611-005-1717-8</span></a>.</p>
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</div>
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<div data-custom-style="Bibliography">
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<p>Andersen RS (2015) <em>Remediating Security</em>. 1. oplag.
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Ph.d.-serien / københavns universitet, institut for statskundskab. Kbh.:
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Københavns Universitet, Institut for Statskundskab.</p>
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</div>
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<div data-custom-style="Bibliography">
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<p>Andersen RS (2018) The art of questioning lethal vision: Mosse’s
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infra and militarized machine vision. In: <em>_Proceeding of EVA
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copenhagen 2018_</em>, 2018.</p>
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</div>
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<div data-custom-style="Bibliography">
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<p>Andrejevic M and Burdon M (2015) Defining the sensor society.
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<em>Television & New Media</em> 16(1): 19–36. DOI: <a
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data-custom-style="Hyperlink">10.1177/1527476414541552</span></a>.
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</p>
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</div>
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<p>Aradau C and Blanke T (2015) The (big) data-security assemblage:
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Knowledge and critique. <em>Big Data & Society</em> 2(2):
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data-custom-style="Hyperlink">10.1177/2053951715609066</span></a>.</p>
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</div>
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<div data-custom-style="Bibliography">
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<p>Aradau C and Blanke T (2018) Governing others: Anomaly and the
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algorithmic subject of security. <em>European Journal of International
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Security</em> 3(1). Cambridge University Press: 1–21. DOI: <a
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href="https://doi.org/10.1017/eis.2017.14"><span
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||||||
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data-custom-style="Hyperlink">10.1017/eis.2017.14</span></a>.</p>
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</div>
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<div data-custom-style="Bibliography">
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</div>
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</section>
|
||||||
|
<section class="footnotes footnotes-end-of-document" role="doc-endnotes">
|
||||||
|
<hr />
|
||||||
|
<ol>
|
||||||
|
<li id="fn1" role="doc-endnote">
|
||||||
|
<div data-custom-style="Footnote Text">
|
||||||
|
<p><span data-custom-style="Footnote Characters"></span> The interface
|
||||||
|
software and code is available at <a
|
||||||
|
href="https://git.rubenvandeven.com/security_vision/svganim"><span
|
||||||
|
data-custom-style="Hyperlink">https://git.rubenvandeven.com/security_vision/svganim</span></a>
|
||||||
|
and <a href="https://gitlab.com/security-vision/chronodiagram"><span
|
||||||
|
data-custom-style="Hyperlink">https://gitlab.com/security-vision/chronodiagram</span></a>
|
||||||
|
</p>
|
||||||
|
</div>
|
||||||
|
<a href="#fnref1" class="footnote-back" role="doc-backlink">↩︎</a>
|
||||||
|
</li>
|
||||||
|
<li id="fn2" role="doc-endnote">
|
||||||
|
<div data-custom-style="Footnote Text">
|
||||||
|
<p><span data-custom-style="Footnote Characters"></span> The interviews
|
||||||
|
were conducted in several European countries: the majority in the
|
||||||
|
Netherlands, but also in Belgium, Hungary and Poland. Based on an
|
||||||
|
initial survey of algorithmic security vision practices in Europe we
|
||||||
|
identified various roles that are involved in such practices. Being a
|
||||||
|
rather small group of people, these interviewees do not serve as
|
||||||
|
“illustrative representatives” (Mol & Law 2002, 16-17) of the field
|
||||||
|
they work in. However, as the interviewees have different cultural and
|
||||||
|
institutional affiliations, and hold different positions in working with
|
||||||
|
algorithms, vision and security, they cover a wide spectrum of
|
||||||
|
engagements with our research object.</p>
|
||||||
|
</div>
|
||||||
|
<a href="#fnref2" class="footnote-back" role="doc-backlink">↩︎</a>
|
||||||
|
</li>
|
||||||
|
<li id="fn3" role="doc-endnote">
|
||||||
|
<div data-custom-style="Footnote Text">
|
||||||
|
<p><span data-custom-style="Footnote Characters"></span> The interviews
|
||||||
|
were conducted by the first two authors, and at a later stage by Clemens
|
||||||
|
Baier. The conversations were largely unstructured, but began with two
|
||||||
|
basic questions. First, we asked the interviewees if they use diagrams
|
||||||
|
in their daily practice. We then asked: “when we speak of ‘security
|
||||||
|
vision’ we speak of the use of computer vision in a security context.
|
||||||
|
Can you explain from your perspective what these concepts mean and how
|
||||||
|
they come together?” After the first few interviews, we identified some
|
||||||
|
recurrent themes, which we then specifically asked later interviewees to
|
||||||
|
discuss.</p>
|
||||||
|
</div>
|
||||||
|
<a href="#fnref3" class="footnote-back" role="doc-backlink">↩︎</a>
|
||||||
|
</li>
|
||||||
|
<li id="fn4" role="doc-endnote">
|
||||||
|
<div data-custom-style="Footnote Text">
|
||||||
|
<p><span data-custom-style="Footnote Characters"></span> Using
|
||||||
|
anthropomorphizing terms such as “neural networks,” “learning” and
|
||||||
|
“training” to denote algorithmic configurations and processes is
|
||||||
|
suggested to hype “artificial intelligence.” While we support the need
|
||||||
|
for an alternative terminology as proposed by Hunger (2023), here we
|
||||||
|
preserve the language of our interviewees.</p>
|
||||||
|
</div>
|
||||||
|
<a href="#fnref4" class="footnote-back" role="doc-backlink">↩︎</a>
|
||||||
|
</li>
|
||||||
|
</ol>
|
||||||
|
</section>
|
||||||
</body>
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</body>
|
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</html>
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