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drawing intermediate lines and playback icon

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Ruben van de Ven 2023-12-19 08:35:00 +01:00
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@ -44,7 +44,7 @@
margin: 0.5rem auto;
}
.anchor{
.anchor {
cursor: pointer;
}
@ -80,10 +80,45 @@
top: 0;
}
div[data-custom-style='Body Text'] p{
div[data-custom-style='Body Text'] p {
padding: 1em 0;
margin: 0;
background-color: rgba(255,255,255, 0.8);
background-color: rgba(255, 255, 255, 0.8);
}
.anchor{
position: relative;
}
.anchor.active:not(.playing)::before{
content:'⏵';
position: absolute;
width: 40px;
height: 40px;
background:gray;
left: calc(50% - 20px);
top: calc(50% - 20px);
vertical-align: middle;
line-height: 35px;
border-radius: 5px;
color:white;
}
.anchor.active:not(.playing):hover::before{
background:black
}
.anchor.playing:hover::before{
content:'⏸︎';
position: absolute;
width: 40px;
height: 40px;
background:black;
left: calc(50% - 20px);
top: calc(50% - 20px);
vertical-align: middle;
line-height: 35px;
border-radius: 5px;
color:white;
}
</style>
<link rel="stylesheet" href="paper.css" />
@ -116,13 +151,21 @@
}
div.controls{display:none !important;}`
);
console.log(sheet, sheet.rules)
function easeInOutQuart(x) {
return x < 0.5 ? 8 * x * x * x * x : 1 - Math.pow(-2 * x + 2, 4) / 2;
}
function easeInOutBack(x) {
const c1 = 1.70158;
const c2 = c1 * 1.525;
return x < 0.5
? (Math.pow(2 * x, 2) * ((c2 + 1) * 2 * x - c2)) / 2
: (Math.pow(2 * x - 2, 2) * ((c2 + 1) * (x * 2 - 2) + c2) + 2) / 2;
}
let scroll_offsets = []
@ -154,10 +197,11 @@
paths.forEach((el) => el.style.strokeDasharray = Math.ceil(el.getTotalLength()) + 'px');
const lastAnchorEl = anchorEls[anchorEls.length - 1];
console.log(lastAnchorEl)
for (const anchorEl of anchorEls) {
anchorEl.addEventListener('click', ev => playerEls[anchorEl.dataset.i].annotator.playPause());
playerEls[anchorEl.dataset.i].annotator.addEventListener('play', ev => anchorEl.classList.add('playing'));
playerEls[anchorEl.dataset.i].annotator.addEventListener('pause', ev => anchorEl.classList.remove('playing'));
}
for (const player of playerEls) {
player.shadowRoot.adoptedStyleSheets = [sheet];
@ -182,7 +226,7 @@
let source_pos, target_pos, source_scale, target_scale, source_color, target_color, source_x_offset, target_x_offset;
const x_column_width = window.innerWidth - document.body.getBoundingClientRect().width+200; // for some reason the 200 is neccesary
const x_column_width = window.innerWidth - document.body.getBoundingClientRect().width + 200; // for some reason the 200 is neccesary
const x_center_map = x_column_width / 2;
const x_center_column = document.body.getBoundingClientRect().left + document.body.getBoundingClientRect().width / 2;
@ -231,6 +275,7 @@
const t = Math.min(1, Math.max(0, (center_y - prev[0]) / (next[0] - prev[0])))
t_ease = easeInOutQuart(t)
// t_ease = easeInOutBack(t)
const dx = target_pos[0] - source_pos[0];
const dy = target_pos[1] - source_pos[1];
@ -246,7 +291,6 @@
y = -1 * (source_pos[1] + dy * t_ease) * scale + window.innerHeight / 2;
const color = (target_color - source_color) * t_ease + source_color
console.log(sheet)
// sheet.rules[0].style.setProperty('--override-color', `rgba(${color},${color},${color},0.7)`);
sheet.rules[0].style.setProperty('--disactive-path', `rgba(${color},${color},${color},0.7)`);
@ -259,11 +303,11 @@
else {
// no paths
paths.forEach((el, idx) => {
if (idx >= step_idx ) {
if (idx >= step_idx) {
el.style.strokeDashoffset = Math.ceil(el.getTotalLength()) + 'px';
} else if (idx == step_idx-1 ) {
} else if (idx == step_idx - 1) {
// console.log('anim', el)
el.style.strokeDashoffset = Math.ceil(el.getTotalLength() - el.getTotalLength()*t_ease) + 'px';
el.style.strokeDashoffset = Math.ceil(el.getTotalLength() - el.getTotalLength() * t_ease) + 'px';
} else {
el.style.strokeDashoffset = 0;
}
@ -282,7 +326,6 @@
// calculate whether we're nearing the conlusion, and color accordingly
const last = Math.max(...Array.from(anchorEls).map((e) => e.getBoundingClientRect().bottom))
console.log(last)
if (last < 0) {
for (const playerEl of playerEls) {
playerEl.classList.add('ending')
@ -307,7 +350,7 @@
let options = {
// root: document.querySelector("#scrollArea"), // viewport by default
rootMargin: `${-Math.round(innerHeight / 3)}px`, //"0px",
rootMargin: `${-Math.floor((window.innerHeight-10) / 2)}px 0px`, //"0px",
threshold: 0,
};
@ -320,10 +363,12 @@
}
playerEl = windowEl.children[index];
if (entry.isIntersecting) {
entry.target.classList.add('active');
playerEl.classList.add('active')
} else {
entry.target.classList.remove('active');
playerEl.classList.remove('active')
if(typeof playerEl.annotator.paused !== 'undefined' && !playerEl.annotator.paused){
if (typeof playerEl.annotator.paused !== 'undefined' && !playerEl.annotator.paused) {
console.log('pause', playerEl.annotator, playerEl.annotator.paused)
playerEl.annotator.pause()
}
@ -335,7 +380,7 @@
for (const anchorEl of anchorEls) {
observer.observe(anchorEl)
}
console.log(anchorEls)
// console.log(anchorEls)
// .forEach(el => observer.observe());
@ -470,6 +515,8 @@
</div>
</div>
<section id="part1">
<p> .... this is a demo to showcase how the chronodiagramming looks like in its interactive form. Please note
that this demo of the interface is not compatible with mobile devices ...</p>
<section id="managing-error-from-the-sublime-to-the-risky-algorithm" class="level2">
<h2>3. Managing error: from the sublime to the risky algorithm</h2>
<div data-custom-style="Body Text">
@ -835,6 +882,602 @@
visibilization, invisibilization, responsibility and accountability.</p>
</div>
</section>
</section>
<section id="a-diagram-of-research" class="level1">
<h1>A diagram of research</h1>
<div data-custom-style="Body Text">
<p>In this conclusion, we reflect upon a final dimension of the method
of diagraming in the context of figurations and configurations: its
potential as an alternative to the conventional research program.</p>
</div>
<div data-custom-style="Body Text">
<p>While writing this text, indeed, the search for a coherent structure
through which we could map the problems that emerged from analyzing the
diagrams in a straightforward narrative proved elusive. We considered
various organizational frameworks, but consistently encountered
resistance from one or two sections. It became evident that our
interviews yielded a rhizome of interrelated problems, creating a
multitude of possible inquiries and overlapping trajectories. Some
dimensions of these problems are related, but not to every problem.</p>
</div>
<div data-custom-style="Body Text">
<p>If we take for example the understanding of algorithmic security
vision as practices of error management as a starting point, we see how
the actors we interviewed have incorporated the societal critique of
algorithmic bias. This serves as a catalyst for novel strategies aimed
at mitigating the repercussions of imperfect systems. The societal
critique has driven the development of synthetic datasets, which promise
equitable representation across diverse demographic groups. It has also
been the reason for the reliance on institutionalized benchmarks to
assess the impartiality of algorithms. Moreover, different
configurations of the human-in-the-loop emerge, all promised to rectify
algorithmic fallibility. We see a causal chain there.</p>
</div>
<div data-custom-style="Body Text">
<p>But how does the question of algorithmic error relate to the shift
from photographic to cinematic vision that algorithmic security vision
brings about? Certainly, there are reverberations. The relegation of
stable identity that we outlined, could be seen as a way to mitigate the
impact of those errors. But it would be a leap to identify these
questions of error as the central driver for the increased incorporation
of moving images in algorithmic security vision.</p>
</div>
<div data-custom-style="Body Text">
<p>However, if we take as our starting point the formidable strides in
computing power and the advancements in camera technologies, we face
similar problems. These developments make the analysis of movement
possible while helping to elucidate the advances in the real-time
analysis that are required to remove the human-in-the-loop, as trialed
in the Burglary-Free Neighborhood. These developments account for the
feasibility of the synthetic data generation, a computing-intense
process which opens a vast horizon of possibilities for developers to
detect objects or actions. Such an account, however, does not address
the need for such a synthetic dataset. A focus on the computation of
movement, however, would highlight how a lack of training data
necessitates many of the practices described. Synthetic data is
necessitated by the glaring absence of pre-existing security datasets
that contain moving bodies. While facial recognition algorithms could be
trained and operated on quickly repurposed photographic datasets of
national identity cards or drivers’ license registries, no dataset for
moving bodies has been available to be repurposed by states or
corporations. This absence of training data requires programmers to
stage scenes for the camera. Thus, while one issue contains echoes of
the other, the network of interrelated problematizations cannot be
flattened into a single narrative.</p>
</div>
<div data-custom-style="Body Text">
<p>The constraints imposed by the linear structure of an academic
article certainly necessitate a specific ordering of sections. Yet the
different research directions we highlight form something else. The
multiple figurations analyzed here generate fresh tensions when put in
relation with security and political practices. What appears from the
diagrams is a network of figurations in various configurations. Instead
of a research <em>program</em>, our interviews point toward a larger
research <em>diagram</em> of interrelated questions, which invites us to
think in terms of pathways through this dynamic and evolving network of
relations.</p>
</div>
</section>
<section id="references" class="level1">
<h1>References</h1>
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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 &amp; 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>
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