working on Publications
This commit is contained in:
69
_site/assets/js/lunr/lunr-en.js
Normal file
69
_site/assets/js/lunr/lunr-en.js
Normal file
@@ -0,0 +1,69 @@
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var idx = lunr(function () {
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this.field('title')
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this.field('excerpt')
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this.field('categories')
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this.field('tags')
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this.ref('id')
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this.pipeline.remove(lunr.trimmer)
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for (var item in store) {
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this.add({
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title: store[item].title,
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excerpt: store[item].excerpt,
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categories: store[item].categories,
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tags: store[item].tags,
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id: item
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})
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}
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});
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$(document).ready(function() {
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$('input#search').on('keyup', function () {
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var resultdiv = $('#results');
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var query = $(this).val().toLowerCase();
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var result =
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idx.query(function (q) {
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query.split(lunr.tokenizer.separator).forEach(function (term) {
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q.term(term, { boost: 100 })
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if(query.lastIndexOf(" ") != query.length-1){
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q.term(term, { usePipeline: false, wildcard: lunr.Query.wildcard.TRAILING, boost: 10 })
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}
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if (term != ""){
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q.term(term, { usePipeline: false, editDistance: 1, boost: 1 })
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}
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})
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});
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resultdiv.empty();
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resultdiv.prepend('<p class="results__found">'+result.length+' Result(s) found</p>');
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for (var item in result) {
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var ref = result[item].ref;
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if(store[ref].teaser){
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var searchitem =
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'<div class="list__item">'+
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'<article class="archive__item" itemscope itemtype="https://schema.org/CreativeWork">'+
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'<h2 class="archive__item-title" itemprop="headline">'+
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'<a href="'+store[ref].url+'" rel="permalink">'+store[ref].title+'</a>'+
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'</h2>'+
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'<div class="archive__item-teaser">'+
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'<img src="'+store[ref].teaser+'" alt="">'+
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'</div>'+
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'<p class="archive__item-excerpt" itemprop="description">'+store[ref].excerpt.split(" ").splice(0,20).join(" ")+'...</p>'+
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'</article>'+
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'</div>';
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}
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else{
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var searchitem =
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'<div class="list__item">'+
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'<article class="archive__item" itemscope itemtype="https://schema.org/CreativeWork">'+
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'<h2 class="archive__item-title" itemprop="headline">'+
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'<a href="'+store[ref].url+'" rel="permalink">'+store[ref].title+'</a>'+
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'</h2>'+
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'<p class="archive__item-excerpt" itemprop="description">'+store[ref].excerpt.split(" ").splice(0,20).join(" ")+'...</p>'+
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'</article>'+
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'</div>';
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}
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resultdiv.append(searchitem);
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}
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});
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});
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522
_site/assets/js/lunr/lunr-gr.js
Normal file
522
_site/assets/js/lunr/lunr-gr.js
Normal file
@@ -0,0 +1,522 @@
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step1list = new Array();
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step1list["ΦΑΓΙΑ"] = "ΦΑ";
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step1list["ΦΑΓΙΟΥ"] = "ΦΑ";
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step1list["ΦΑΓΙΩΝ"] = "ΦΑ";
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step1list["ΣΚΑΓΙΑ"] = "ΣΚΑ";
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step1list["ΣΚΑΓΙΟΥ"] = "ΣΚΑ";
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step1list["ΣΚΑΓΙΩΝ"] = "ΣΚΑ";
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step1list["ΟΛΟΓΙΟΥ"] = "ΟΛΟ";
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step1list["ΟΛΟΓΙΑ"] = "ΟΛΟ";
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step1list["ΟΛΟΓΙΩΝ"] = "ΟΛΟ";
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step1list["ΣΟΓΙΟΥ"] = "ΣΟ";
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step1list["ΣΟΓΙΑ"] = "ΣΟ";
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step1list["ΣΟΓΙΩΝ"] = "ΣΟ";
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step1list["ΤΑΤΟΓΙΑ"] = "ΤΑΤΟ";
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step1list["ΤΑΤΟΓΙΟΥ"] = "ΤΑΤΟ";
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step1list["ΤΑΤΟΓΙΩΝ"] = "ΤΑΤΟ";
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step1list["ΚΡΕΑΣ"] = "ΚΡΕ";
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step1list["ΚΡΕΑΤΟΣ"] = "ΚΡΕ";
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step1list["ΚΡΕΑΤΑ"] = "ΚΡΕ";
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step1list["ΚΡΕΑΤΩΝ"] = "ΚΡΕ";
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step1list["ΠΕΡΑΣ"] = "ΠΕΡ";
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step1list["ΠΕΡΑΤΟΣ"] = "ΠΕΡ";
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step1list["ΠΕΡΑΤΑ"] = "ΠΕΡ";
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step1list["ΠΕΡΑΤΩΝ"] = "ΠΕΡ";
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step1list["ΤΕΡΑΣ"] = "ΤΕΡ";
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step1list["ΤΕΡΑΤΟΣ"] = "ΤΕΡ";
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step1list["ΤΕΡΑΤΑ"] = "ΤΕΡ";
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step1list["ΤΕΡΑΤΩΝ"] = "ΤΕΡ";
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step1list["ΦΩΣ"] = "ΦΩ";
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step1list["ΦΩΤΟΣ"] = "ΦΩ";
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step1list["ΦΩΤΑ"] = "ΦΩ";
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step1list["ΦΩΤΩΝ"] = "ΦΩ";
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step1list["ΚΑΘΕΣΤΩΣ"] = "ΚΑΘΕΣΤ";
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step1list["ΚΑΘΕΣΤΩΤΟΣ"] = "ΚΑΘΕΣΤ";
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step1list["ΚΑΘΕΣΤΩΤΑ"] = "ΚΑΘΕΣΤ";
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step1list["ΚΑΘΕΣΤΩΤΩΝ"] = "ΚΑΘΕΣΤ";
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step1list["ΓΕΓΟΝΟΣ"] = "ΓΕΓΟΝ";
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step1list["ΓΕΓΟΝΟΤΟΣ"] = "ΓΕΓΟΝ";
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step1list["ΓΕΓΟΝΟΤΑ"] = "ΓΕΓΟΝ";
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step1list["ΓΕΓΟΝΟΤΩΝ"] = "ΓΕΓΟΝ";
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v = "[ΑΕΗΙΟΥΩ]";
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v2 = "[ΑΕΗΙΟΩ]"
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function stemWord(w) {
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var stem;
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var suffix;
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var firstch;
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var origword = w;
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test1 = new Boolean(true);
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if(w.length < 4) {
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return w;
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}
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var re;
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var re2;
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var re3;
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var re4;
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re = /(.*)(ΦΑΓΙΑ|ΦΑΓΙΟΥ|ΦΑΓΙΩΝ|ΣΚΑΓΙΑ|ΣΚΑΓΙΟΥ|ΣΚΑΓΙΩΝ|ΟΛΟΓΙΟΥ|ΟΛΟΓΙΑ|ΟΛΟΓΙΩΝ|ΣΟΓΙΟΥ|ΣΟΓΙΑ|ΣΟΓΙΩΝ|ΤΑΤΟΓΙΑ|ΤΑΤΟΓΙΟΥ|ΤΑΤΟΓΙΩΝ|ΚΡΕΑΣ|ΚΡΕΑΤΟΣ|ΚΡΕΑΤΑ|ΚΡΕΑΤΩΝ|ΠΕΡΑΣ|ΠΕΡΑΤΟΣ|ΠΕΡΑΤΑ|ΠΕΡΑΤΩΝ|ΤΕΡΑΣ|ΤΕΡΑΤΟΣ|ΤΕΡΑΤΑ|ΤΕΡΑΤΩΝ|ΦΩΣ|ΦΩΤΟΣ|ΦΩΤΑ|ΦΩΤΩΝ|ΚΑΘΕΣΤΩΣ|ΚΑΘΕΣΤΩΤΟΣ|ΚΑΘΕΣΤΩΤΑ|ΚΑΘΕΣΤΩΤΩΝ|ΓΕΓΟΝΟΣ|ΓΕΓΟΝΟΤΟΣ|ΓΕΓΟΝΟΤΑ|ΓΕΓΟΝΟΤΩΝ)$/;
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if(re.test(w)) {
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var fp = re.exec(w);
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stem = fp[1];
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suffix = fp[2];
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w = stem + step1list[suffix];
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test1 = false;
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}
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re = /^(.+?)(ΑΔΕΣ|ΑΔΩΝ)$/;
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if(re.test(w)) {
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var fp = re.exec(w);
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stem = fp[1];
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w = stem;
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reg1 = /(ΟΚ|ΜΑΜ|ΜΑΝ|ΜΠΑΜΠ|ΠΑΤΕΡ|ΓΙΑΓΙ|ΝΤΑΝΤ|ΚΥΡ|ΘΕΙ|ΠΕΘΕΡ)$/;
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if(!(reg1.test(w))) {
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w = w + "ΑΔ";
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}
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}
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re2 = /^(.+?)(ΕΔΕΣ|ΕΔΩΝ)$/;
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if(re2.test(w)) {
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var fp = re2.exec(w);
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stem = fp[1];
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w = stem;
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exept2 = /(ΟΠ|ΙΠ|ΕΜΠ|ΥΠ|ΓΗΠ|ΔΑΠ|ΚΡΑΣΠ|ΜΙΛ)$/;
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if(exept2.test(w)) {
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w = w + "ΕΔ";
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}
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}
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re3 = /^(.+?)(ΟΥΔΕΣ|ΟΥΔΩΝ)$/;
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if(re3.test(w)) {
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var fp = re3.exec(w);
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stem = fp[1];
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w = stem;
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exept3 = /(ΑΡΚ|ΚΑΛΙΑΚ|ΠΕΤΑΛ|ΛΙΧ|ΠΛΕΞ|ΣΚ|Σ|ΦΛ|ΦΡ|ΒΕΛ|ΛΟΥΛ|ΧΝ|ΣΠ|ΤΡΑΓ|ΦΕ)$/;
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if(exept3.test(w)) {
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w = w + "ΟΥΔ";
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}
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}
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re4 = /^(.+?)(ΕΩΣ|ΕΩΝ)$/;
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if(re4.test(w)) {
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var fp = re4.exec(w);
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stem = fp[1];
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w = stem;
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test1 = false;
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exept4 = /^(Θ|Δ|ΕΛ|ΓΑΛ|Ν|Π|ΙΔ|ΠΑΡ)$/;
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if(exept4.test(w)) {
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w = w + "Ε";
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}
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}
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re = /^(.+?)(ΙΑ|ΙΟΥ|ΙΩΝ)$/;
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if(re.test(w)) {
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var fp = re.exec(w);
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stem = fp[1];
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w = stem;
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re2 = new RegExp(v + "$");
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test1 = false;
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if(re2.test(w)) {
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w = stem + "Ι";
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}
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}
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re = /^(.+?)(ΙΚΑ|ΙΚΟ|ΙΚΟΥ|ΙΚΩΝ)$/;
|
||||
|
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if(re.test(w)) {
|
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var fp = re.exec(w);
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stem = fp[1];
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||||
w = stem;
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test1 = false;
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re2 = new RegExp(v + "$");
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exept5 = /^(ΑΛ|ΑΔ|ΕΝΔ|ΑΜΑΝ|ΑΜΜΟΧΑΛ|ΗΘ|ΑΝΗΘ|ΑΝΤΙΔ|ΦΥΣ|ΒΡΩΜ|ΓΕΡ|ΕΞΩΔ|ΚΑΛΠ|ΚΑΛΛΙΝ|ΚΑΤΑΔ|ΜΟΥΛ|ΜΠΑΝ|ΜΠΑΓΙΑΤ|ΜΠΟΛ|ΜΠΟΣ|ΝΙΤ|ΞΙΚ|ΣΥΝΟΜΗΛ|ΠΕΤΣ|ΠΙΤΣ|ΠΙΚΑΝΤ|ΠΛΙΑΤΣ|ΠΟΣΤΕΛΝ|ΠΡΩΤΟΔ|ΣΕΡΤ|ΣΥΝΑΔ|ΤΣΑΜ|ΥΠΟΔ|ΦΙΛΟΝ|ΦΥΛΟΔ|ΧΑΣ)$/;
|
||||
|
||||
if((exept5.test(w)) || (re2.test(w))) {
|
||||
w = w + "ΙΚ";
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}
|
||||
}
|
||||
|
||||
re = /^(.+?)(ΑΜΕ)$/;
|
||||
re2 = /^(.+?)(ΑΓΑΜΕ|ΗΣΑΜΕ|ΟΥΣΑΜΕ|ΗΚΑΜΕ|ΗΘΗΚΑΜΕ)$/;
|
||||
if(w == "ΑΓΑΜΕ") {
|
||||
w = "ΑΓΑΜ";
|
||||
}
|
||||
|
||||
if(re2.test(w)) {
|
||||
var fp = re2.exec(w);
|
||||
stem = fp[1];
|
||||
w = stem;
|
||||
test1 = false;
|
||||
}
|
||||
|
||||
if(re.test(w)) {
|
||||
var fp = re.exec(w);
|
||||
stem = fp[1];
|
||||
w = stem;
|
||||
test1 = false;
|
||||
|
||||
exept6 = /^(ΑΝΑΠ|ΑΠΟΘ|ΑΠΟΚ|ΑΠΟΣΤ|ΒΟΥΒ|ΞΕΘ|ΟΥΛ|ΠΕΘ|ΠΙΚΡ|ΠΟΤ|ΣΙΧ|Χ)$/;
|
||||
|
||||
if(exept6.test(w)) {
|
||||
w = w + "ΑΜ";
|
||||
}
|
||||
}
|
||||
|
||||
re2 = /^(.+?)(ΑΝΕ)$/;
|
||||
re3 = /^(.+?)(ΑΓΑΝΕ|ΗΣΑΝΕ|ΟΥΣΑΝΕ|ΙΟΝΤΑΝΕ|ΙΟΤΑΝΕ|ΙΟΥΝΤΑΝΕ|ΟΝΤΑΝΕ|ΟΤΑΝΕ|ΟΥΝΤΑΝΕ|ΗΚΑΝΕ|ΗΘΗΚΑΝΕ)$/;
|
||||
|
||||
if(re3.test(w)) {
|
||||
var fp = re3.exec(w);
|
||||
stem = fp[1];
|
||||
w = stem;
|
||||
test1 = false;
|
||||
|
||||
re3 = /^(ΤΡ|ΤΣ)$/;
|
||||
|
||||
if(re3.test(w)) {
|
||||
w = w + "ΑΓΑΝ";
|
||||
}
|
||||
}
|
||||
|
||||
if(re2.test(w)) {
|
||||
var fp = re2.exec(w);
|
||||
stem = fp[1];
|
||||
w = stem;
|
||||
test1 = false;
|
||||
|
||||
re2 = new RegExp(v2 + "$");
|
||||
exept7 = /^(ΒΕΤΕΡ|ΒΟΥΛΚ|ΒΡΑΧΜ|Γ|ΔΡΑΔΟΥΜ|Θ|ΚΑΛΠΟΥΖ|ΚΑΣΤΕΛ|ΚΟΡΜΟΡ|ΛΑΟΠΛ|ΜΩΑΜΕΘ|Μ|ΜΟΥΣΟΥΛΜ|Ν|ΟΥΛ|Π|ΠΕΛΕΚ|ΠΛ|ΠΟΛΙΣ|ΠΟΡΤΟΛ|ΣΑΡΑΚΑΤΣ|ΣΟΥΛΤ|ΤΣΑΡΛΑΤ|ΟΡΦ|ΤΣΙΓΓ|ΤΣΟΠ|ΦΩΤΟΣΤΕΦ|Χ|ΨΥΧΟΠΛ|ΑΓ|ΟΡΦ|ΓΑΛ|ΓΕΡ|ΔΕΚ|ΔΙΠΛ|ΑΜΕΡΙΚΑΝ|ΟΥΡ|ΠΙΘ|ΠΟΥΡΙΤ|Σ|ΖΩΝΤ|ΙΚ|ΚΑΣΤ|ΚΟΠ|ΛΙΧ|ΛΟΥΘΗΡ|ΜΑΙΝΤ|ΜΕΛ|ΣΙΓ|ΣΠ|ΣΤΕΓ|ΤΡΑΓ|ΤΣΑΓ|Φ|ΕΡ|ΑΔΑΠ|ΑΘΙΓΓ|ΑΜΗΧ|ΑΝΙΚ|ΑΝΟΡΓ|ΑΠΗΓ|ΑΠΙΘ|ΑΤΣΙΓΓ|ΒΑΣ|ΒΑΣΚ|ΒΑΘΥΓΑΛ|ΒΙΟΜΗΧ|ΒΡΑΧΥΚ|ΔΙΑΤ|ΔΙΑΦ|ΕΝΟΡΓ|ΘΥΣ|ΚΑΠΝΟΒΙΟΜΗΧ|ΚΑΤΑΓΑΛ|ΚΛΙΒ|ΚΟΙΛΑΡΦ|ΛΙΒ|ΜΕΓΛΟΒΙΟΜΗΧ|ΜΙΚΡΟΒΙΟΜΗΧ|ΝΤΑΒ|ΞΗΡΟΚΛΙΒ|ΟΛΙΓΟΔΑΜ|ΟΛΟΓΑΛ|ΠΕΝΤΑΡΦ|ΠΕΡΗΦ|ΠΕΡΙΤΡ|ΠΛΑΤ|ΠΟΛΥΔΑΠ|ΠΟΛΥΜΗΧ|ΣΤΕΦ|ΤΑΒ|ΤΕΤ|ΥΠΕΡΗΦ|ΥΠΟΚΟΠ|ΧΑΜΗΛΟΔΑΠ|ΨΗΛΟΤΑΒ)$/;
|
||||
|
||||
if((re2.test(w)) || (exept7.test(w))) {
|
||||
w = w + "ΑΝ";
|
||||
}
|
||||
}
|
||||
|
||||
re3 = /^(.+?)(ΕΤΕ)$/;
|
||||
re4 = /^(.+?)(ΗΣΕΤΕ)$/;
|
||||
|
||||
if(re4.test(w)) {
|
||||
var fp = re4.exec(w);
|
||||
stem = fp[1];
|
||||
w = stem;
|
||||
test1 = false;
|
||||
}
|
||||
|
||||
if(re3.test(w)) {
|
||||
var fp = re3.exec(w);
|
||||
stem = fp[1];
|
||||
w = stem;
|
||||
test1 = false;
|
||||
|
||||
re3 = new RegExp(v2 + "$");
|
||||
exept8 = /(ΟΔ|ΑΙΡ|ΦΟΡ|ΤΑΘ|ΔΙΑΘ|ΣΧ|ΕΝΔ|ΕΥΡ|ΤΙΘ|ΥΠΕΡΘ|ΡΑΘ|ΕΝΘ|ΡΟΘ|ΣΘ|ΠΥΡ|ΑΙΝ|ΣΥΝΔ|ΣΥΝ|ΣΥΝΘ|ΧΩΡ|ΠΟΝ|ΒΡ|ΚΑΘ|ΕΥΘ|ΕΚΘ|ΝΕΤ|ΡΟΝ|ΑΡΚ|ΒΑΡ|ΒΟΛ|ΩΦΕΛ)$/;
|
||||
exept9 = /^(ΑΒΑΡ|ΒΕΝ|ΕΝΑΡ|ΑΒΡ|ΑΔ|ΑΘ|ΑΝ|ΑΠΛ|ΒΑΡΟΝ|ΝΤΡ|ΣΚ|ΚΟΠ|ΜΠΟΡ|ΝΙΦ|ΠΑΓ|ΠΑΡΑΚΑΛ|ΣΕΡΠ|ΣΚΕΛ|ΣΥΡΦ|ΤΟΚ|Υ|Δ|ΕΜ|ΘΑΡΡ|Θ)$/;
|
||||
|
||||
if((re3.test(w)) || (exept8.test(w)) || (exept9.test(w))) {
|
||||
w = w + "ΕΤ";
|
||||
}
|
||||
}
|
||||
|
||||
re = /^(.+?)(ΟΝΤΑΣ|ΩΝΤΑΣ)$/;
|
||||
|
||||
if(re.test(w)) {
|
||||
var fp = re.exec(w);
|
||||
stem = fp[1];
|
||||
w = stem;
|
||||
test1 = false;
|
||||
|
||||
exept10 = /^(ΑΡΧ)$/;
|
||||
exept11 = /(ΚΡΕ)$/;
|
||||
if(exept10.test(w)) {
|
||||
w = w + "ΟΝΤ";
|
||||
}
|
||||
if(exept11.test(w)) {
|
||||
w = w + "ΩΝΤ";
|
||||
}
|
||||
}
|
||||
|
||||
re = /^(.+?)(ΟΜΑΣΤΕ|ΙΟΜΑΣΤΕ)$/;
|
||||
|
||||
if(re.test(w)) {
|
||||
var fp = re.exec(w);
|
||||
stem = fp[1];
|
||||
w = stem;
|
||||
test1 = false;
|
||||
|
||||
exept11 = /^(ΟΝ)$/;
|
||||
|
||||
if(exept11.test(w)) {
|
||||
w = w + "ΟΜΑΣΤ";
|
||||
}
|
||||
}
|
||||
|
||||
re = /^(.+?)(ΕΣΤΕ)$/;
|
||||
re2 = /^(.+?)(ΙΕΣΤΕ)$/;
|
||||
|
||||
if(re2.test(w)) {
|
||||
var fp = re2.exec(w);
|
||||
stem = fp[1];
|
||||
w = stem;
|
||||
test1 = false;
|
||||
|
||||
re2 = /^(Π|ΑΠ|ΣΥΜΠ|ΑΣΥΜΠ|ΑΚΑΤΑΠ|ΑΜΕΤΑΜΦ)$/;
|
||||
|
||||
if(re2.test(w)) {
|
||||
w = w + "ΙΕΣΤ";
|
||||
}
|
||||
}
|
||||
|
||||
if(re.test(w)) {
|
||||
var fp = re.exec(w);
|
||||
stem = fp[1];
|
||||
w = stem;
|
||||
test1 = false;
|
||||
|
||||
exept12 = /^(ΑΛ|ΑΡ|ΕΚΤΕΛ|Ζ|Μ|Ξ|ΠΑΡΑΚΑΛ|ΑΡ|ΠΡΟ|ΝΙΣ)$/;
|
||||
|
||||
if(exept12.test(w)) {
|
||||
w = w + "ΕΣΤ";
|
||||
}
|
||||
}
|
||||
|
||||
re = /^(.+?)(ΗΚΑ|ΗΚΕΣ|ΗΚΕ)$/;
|
||||
re2 = /^(.+?)(ΗΘΗΚΑ|ΗΘΗΚΕΣ|ΗΘΗΚΕ)$/;
|
||||
|
||||
if(re2.test(w)) {
|
||||
var fp = re2.exec(w);
|
||||
stem = fp[1];
|
||||
w = stem;
|
||||
test1 = false;
|
||||
}
|
||||
|
||||
if(re.test(w)) {
|
||||
var fp = re.exec(w);
|
||||
stem = fp[1];
|
||||
w = stem;
|
||||
test1 = false;
|
||||
|
||||
exept13 = /(ΣΚΩΛ|ΣΚΟΥΛ|ΝΑΡΘ|ΣΦ|ΟΘ|ΠΙΘ)$/;
|
||||
exept14 = /^(ΔΙΑΘ|Θ|ΠΑΡΑΚΑΤΑΘ|ΠΡΟΣΘ|ΣΥΝΘ|)$/;
|
||||
|
||||
if((exept13.test(w)) || (exept14.test(w))) {
|
||||
w = w + "ΗΚ";
|
||||
}
|
||||
}
|
||||
|
||||
re = /^(.+?)(ΟΥΣΑ|ΟΥΣΕΣ|ΟΥΣΕ)$/;
|
||||
|
||||
if(re.test(w)) {
|
||||
var fp = re.exec(w);
|
||||
stem = fp[1];
|
||||
w = stem;
|
||||
test1 = false;
|
||||
|
||||
exept15 = /^(ΦΑΡΜΑΚ|ΧΑΔ|ΑΓΚ|ΑΝΑΡΡ|ΒΡΟΜ|ΕΚΛΙΠ|ΛΑΜΠΙΔ|ΛΕΧ|Μ|ΠΑΤ|Ρ|Λ|ΜΕΔ|ΜΕΣΑΖ|ΥΠΟΤΕΙΝ|ΑΜ|ΑΙΘ|ΑΝΗΚ|ΔΕΣΠΟΖ|ΕΝΔΙΑΦΕΡ|ΔΕ|ΔΕΥΤΕΡΕΥ|ΚΑΘΑΡΕΥ|ΠΛΕ|ΤΣΑ)$/;
|
||||
exept16 = /(ΠΟΔΑΡ|ΒΛΕΠ|ΠΑΝΤΑΧ|ΦΡΥΔ|ΜΑΝΤΙΛ|ΜΑΛΛ|ΚΥΜΑΤ|ΛΑΧ|ΛΗΓ|ΦΑΓ|ΟΜ|ΠΡΩΤ)$/;
|
||||
|
||||
if((exept15.test(w)) || (exept16.test(w))) {
|
||||
w = w + "ΟΥΣ";
|
||||
}
|
||||
}
|
||||
|
||||
re = /^(.+?)(ΑΓΑ|ΑΓΕΣ|ΑΓΕ)$/;
|
||||
|
||||
if(re.test(w)) {
|
||||
var fp = re.exec(w);
|
||||
stem = fp[1];
|
||||
w = stem;
|
||||
test1 = false;
|
||||
|
||||
exept17 = /^(ΨΟΦ|ΝΑΥΛΟΧ)$/;
|
||||
exept20 = /(ΚΟΛΛ)$/;
|
||||
exept18 = /^(ΑΒΑΣΤ|ΠΟΛΥΦ|ΑΔΗΦ|ΠΑΜΦ|Ρ|ΑΣΠ|ΑΦ|ΑΜΑΛ|ΑΜΑΛΛΙ|ΑΝΥΣΤ|ΑΠΕΡ|ΑΣΠΑΡ|ΑΧΑΡ|ΔΕΡΒΕΝ|ΔΡΟΣΟΠ|ΞΕΦ|ΝΕΟΠ|ΝΟΜΟΤ|ΟΛΟΠ|ΟΜΟΤ|ΠΡΟΣΤ|ΠΡΟΣΩΠΟΠ|ΣΥΜΠ|ΣΥΝΤ|Τ|ΥΠΟΤ|ΧΑΡ|ΑΕΙΠ|ΑΙΜΟΣΤ|ΑΝΥΠ|ΑΠΟΤ|ΑΡΤΙΠ|ΔΙΑΤ|ΕΝ|ΕΠΙΤ|ΚΡΟΚΑΛΟΠ|ΣΙΔΗΡΟΠ|Λ|ΝΑΥ|ΟΥΛΑΜ|ΟΥΡ|Π|ΤΡ|Μ)$/;
|
||||
exept19 = /(ΟΦ|ΠΕΛ|ΧΟΡΤ|ΛΛ|ΣΦ|ΡΠ|ΦΡ|ΠΡ|ΛΟΧ|ΣΜΗΝ)$/;
|
||||
|
||||
if(((exept18.test(w)) || (exept19.test(w))) && !((exept17.test(w)) || (exept20.test(w)))) {
|
||||
w = w + "ΑΓ";
|
||||
}
|
||||
}
|
||||
|
||||
re = /^(.+?)(ΗΣΕ|ΗΣΟΥ|ΗΣΑ)$/;
|
||||
|
||||
if(re.test(w)) {
|
||||
var fp = re.exec(w);
|
||||
stem = fp[1];
|
||||
w = stem;
|
||||
test1 = false;
|
||||
|
||||
exept21 = /^(Ν|ΧΕΡΣΟΝ|ΔΩΔΕΚΑΝ|ΕΡΗΜΟΝ|ΜΕΓΑΛΟΝ|ΕΠΤΑΝ)$/;
|
||||
|
||||
if(exept21.test(w)) {
|
||||
w = w + "ΗΣ";
|
||||
}
|
||||
}
|
||||
|
||||
re = /^(.+?)(ΗΣΤΕ)$/;
|
||||
|
||||
if(re.test(w)) {
|
||||
var fp = re.exec(w);
|
||||
stem = fp[1];
|
||||
w = stem;
|
||||
test1 = false;
|
||||
|
||||
exept22 = /^(ΑΣΒ|ΣΒ|ΑΧΡ|ΧΡ|ΑΠΛ|ΑΕΙΜΝ|ΔΥΣΧΡ|ΕΥΧΡ|ΚΟΙΝΟΧΡ|ΠΑΛΙΜΨ)$/;
|
||||
|
||||
if(exept22.test(w)) {
|
||||
w = w + "ΗΣΤ";
|
||||
}
|
||||
}
|
||||
|
||||
re = /^(.+?)(ΟΥΝΕ|ΗΣΟΥΝΕ|ΗΘΟΥΝΕ)$/;
|
||||
|
||||
if(re.test(w)) {
|
||||
var fp = re.exec(w);
|
||||
stem = fp[1];
|
||||
w = stem;
|
||||
test1 = false;
|
||||
|
||||
exept23 = /^(Ν|Ρ|ΣΠΙ|ΣΤΡΑΒΟΜΟΥΤΣ|ΚΑΚΟΜΟΥΤΣ|ΕΞΩΝ)$/;
|
||||
|
||||
if(exept23.test(w)) {
|
||||
w = w + "ΟΥΝ";
|
||||
}
|
||||
}
|
||||
|
||||
re = /^(.+?)(ΟΥΜΕ|ΗΣΟΥΜΕ|ΗΘΟΥΜΕ)$/;
|
||||
|
||||
if(re.test(w)) {
|
||||
var fp = re.exec(w);
|
||||
stem = fp[1];
|
||||
w = stem;
|
||||
test1 = false;
|
||||
|
||||
exept24 = /^(ΠΑΡΑΣΟΥΣ|Φ|Χ|ΩΡΙΟΠΛ|ΑΖ|ΑΛΛΟΣΟΥΣ|ΑΣΟΥΣ)$/;
|
||||
|
||||
if(exept24.test(w)) {
|
||||
w = w + "ΟΥΜ";
|
||||
}
|
||||
}
|
||||
|
||||
re = /^(.+?)(ΜΑΤΑ|ΜΑΤΩΝ|ΜΑΤΟΣ)$/;
|
||||
re2 = /^(.+?)(Α|ΑΓΑΤΕ|ΑΓΑΝ|ΑΕΙ|ΑΜΑΙ|ΑΝ|ΑΣ|ΑΣΑΙ|ΑΤΑΙ|ΑΩ|Ε|ΕΙ|ΕΙΣ|ΕΙΤΕ|ΕΣΑΙ|ΕΣ|ΕΤΑΙ|Ι|ΙΕΜΑΙ|ΙΕΜΑΣΤΕ|ΙΕΤΑΙ|ΙΕΣΑΙ|ΙΕΣΑΣΤΕ|ΙΟΜΑΣΤΑΝ|ΙΟΜΟΥΝ|ΙΟΜΟΥΝΑ|ΙΟΝΤΑΝ|ΙΟΝΤΟΥΣΑΝ|ΙΟΣΑΣΤΑΝ|ΙΟΣΑΣΤΕ|ΙΟΣΟΥΝ|ΙΟΣΟΥΝΑ|ΙΟΤΑΝ|ΙΟΥΜΑ|ΙΟΥΜΑΣΤΕ|ΙΟΥΝΤΑΙ|ΙΟΥΝΤΑΝ|Η|ΗΔΕΣ|ΗΔΩΝ|ΗΘΕΙ|ΗΘΕΙΣ|ΗΘΕΙΤΕ|ΗΘΗΚΑΤΕ|ΗΘΗΚΑΝ|ΗΘΟΥΝ|ΗΘΩ|ΗΚΑΤΕ|ΗΚΑΝ|ΗΣ|ΗΣΑΝ|ΗΣΑΤΕ|ΗΣΕΙ|ΗΣΕΣ|ΗΣΟΥΝ|ΗΣΩ|Ο|ΟΙ|ΟΜΑΙ|ΟΜΑΣΤΑΝ|ΟΜΟΥΝ|ΟΜΟΥΝΑ|ΟΝΤΑΙ|ΟΝΤΑΝ|ΟΝΤΟΥΣΑΝ|ΟΣ|ΟΣΑΣΤΑΝ|ΟΣΑΣΤΕ|ΟΣΟΥΝ|ΟΣΟΥΝΑ|ΟΤΑΝ|ΟΥ|ΟΥΜΑΙ|ΟΥΜΑΣΤΕ|ΟΥΝ|ΟΥΝΤΑΙ|ΟΥΝΤΑΝ|ΟΥΣ|ΟΥΣΑΝ|ΟΥΣΑΤΕ|Υ|ΥΣ|Ω|ΩΝ)$/;
|
||||
|
||||
if(re.test(w)) {
|
||||
var fp = re.exec(w);
|
||||
stem = fp[1];
|
||||
w = stem + "ΜΑ";
|
||||
}
|
||||
|
||||
if((re2.test(w)) && (test1)) {
|
||||
var fp = re2.exec(w);
|
||||
stem = fp[1];
|
||||
w = stem;
|
||||
|
||||
}
|
||||
|
||||
re = /^(.+?)(ΕΣΤΕΡ|ΕΣΤΑΤ|ΟΤΕΡ|ΟΤΑΤ|ΥΤΕΡ|ΥΤΑΤ|ΩΤΕΡ|ΩΤΑΤ)$/;
|
||||
|
||||
if(re.test(w)) {
|
||||
var fp = re.exec(w);
|
||||
stem = fp[1];
|
||||
w = stem;
|
||||
}
|
||||
|
||||
return w;
|
||||
};
|
||||
|
||||
var greekStemmer = function (token) {
|
||||
return token.update(function (word) {
|
||||
return stemWord(word);
|
||||
})
|
||||
}
|
||||
|
||||
var idx = lunr(function () {
|
||||
this.field('title')
|
||||
this.field('excerpt')
|
||||
this.field('categories')
|
||||
this.field('tags')
|
||||
this.ref('id')
|
||||
|
||||
this.pipeline.remove(lunr.trimmer)
|
||||
this.pipeline.add(greekStemmer)
|
||||
this.pipeline.remove(lunr.stemmer)
|
||||
|
||||
for (var item in store) {
|
||||
this.add({
|
||||
title: store[item].title,
|
||||
excerpt: store[item].excerpt,
|
||||
categories: store[item].categories,
|
||||
tags: store[item].tags,
|
||||
id: item
|
||||
})
|
||||
}
|
||||
});
|
||||
|
||||
$(document).ready(function() {
|
||||
$('input#search').on('keyup', function () {
|
||||
var resultdiv = $('#results');
|
||||
var query = $(this).val().toLowerCase();
|
||||
var result =
|
||||
idx.query(function (q) {
|
||||
query.split(lunr.tokenizer.separator).forEach(function (term) {
|
||||
q.term(term, { boost: 100 })
|
||||
if(query.lastIndexOf(" ") != query.length-1){
|
||||
q.term(term, { usePipeline: false, wildcard: lunr.Query.wildcard.TRAILING, boost: 10 })
|
||||
}
|
||||
if (term != ""){
|
||||
q.term(term, { usePipeline: false, editDistance: 1, boost: 1 })
|
||||
}
|
||||
})
|
||||
});
|
||||
resultdiv.empty();
|
||||
resultdiv.prepend('<p class="results__found">'+result.length+' Result(s) found</p>');
|
||||
for (var item in result) {
|
||||
var ref = result[item].ref;
|
||||
if(store[ref].teaser){
|
||||
var searchitem =
|
||||
'<div class="list__item">'+
|
||||
'<article class="archive__item" itemscope itemtype="https://schema.org/CreativeWork">'+
|
||||
'<h2 class="archive__item-title" itemprop="headline">'+
|
||||
'<a href="'+store[ref].url+'" rel="permalink">'+store[ref].title+'</a>'+
|
||||
'</h2>'+
|
||||
'<div class="archive__item-teaser">'+
|
||||
'<img src="'+store[ref].teaser+'" alt="">'+
|
||||
'</div>'+
|
||||
'<p class="archive__item-excerpt" itemprop="description">'+store[ref].excerpt.split(" ").splice(0,20).join(" ")+'...</p>'+
|
||||
'</article>'+
|
||||
'</div>';
|
||||
}
|
||||
else{
|
||||
var searchitem =
|
||||
'<div class="list__item">'+
|
||||
'<article class="archive__item" itemscope itemtype="https://schema.org/CreativeWork">'+
|
||||
'<h2 class="archive__item-title" itemprop="headline">'+
|
||||
'<a href="'+store[ref].url+'" rel="permalink">'+store[ref].title+'</a>'+
|
||||
'</h2>'+
|
||||
'<p class="archive__item-excerpt" itemprop="description">'+store[ref].excerpt.split(" ").splice(0,20).join(" ")+'...</p>'+
|
||||
'</article>'+
|
||||
'</div>';
|
||||
}
|
||||
resultdiv.append(searchitem);
|
||||
}
|
||||
});
|
||||
});
|
||||
235
_site/assets/js/lunr/lunr-store.js
Normal file
235
_site/assets/js/lunr/lunr-store.js
Normal file
@@ -0,0 +1,235 @@
|
||||
var store = [{
|
||||
"title": "InnoMi Project",
|
||||
"excerpt":"The InnoMi research initiative served as a vital bridge between academic research and industrial application within Bavaria. Funded by the state government and operating under the umbrella of the Zentrum Digitalisierung.Bayern, the project provided crucial resources and a collaborative framework. Project: InnoMi - Innovations for the Mobile Internet Affiliation: Zentrum...","categories": ["projects"],
|
||||
"tags": [],
|
||||
"url": "/projects/innomi/",
|
||||
"teaser": "/assets/images/projects/innomi.png"
|
||||
},{
|
||||
"title": "Computer Architecture TA",
|
||||
"excerpt":"During my doctoral studies at LMU Munich, I served as a Teaching Assistant and took on significant organizational responsibilities for the undergraduate lecture “Rechnerarchitektur” (Computer Architecture). This foundational course, led by Prof. Dr. Linnhoff-Popien, catered to approximately 600 students each semester. My primary responsibilities focused on managing the tutorial component...","categories": ["teaching"],
|
||||
"tags": [],
|
||||
"url": "/teaching/computer_achitecture/",
|
||||
"teaser": "/assets/images/teaching/computer_gear.png"
|
||||
},{
|
||||
"title": "IoT Practical Exercise",
|
||||
"excerpt":"As part of the lecture Internet of Things (IoT): Devices, Connectivity, and Services, I was responsible for designing and conducting a practical programming exercise suitable for completion within one to two class sessions. This exercise targeted approximately 200 students during the Winter Semester 2018/19. The goal was to provide hands-on...","categories": ["teaching"],
|
||||
"tags": ["teaching","iot","mqtt","python","influxdb","distributed-systems","practical-course"],
|
||||
"url": "/teaching/IOT/",
|
||||
"teaser": "/assets/images/teaching/server.png"
|
||||
},{
|
||||
"title": "Python 101 Course",
|
||||
"excerpt":"In preparation for the practical exercises within the Internet of Things (IoT) lecture series, we identified the need for foundational programming skills among the student cohort. Consequently, during the Winter Semester 2018/19, I co-developed and co-taught an intensive introductory course focused on the Python programming language. This “Python 101” module,...","categories": ["teaching"],
|
||||
"tags": ["teaching","python","programming","introductory-course","curriculum-development"],
|
||||
"url": "/teaching/Python/",
|
||||
"teaser": "/assets/images/teaching/py.png"
|
||||
},{
|
||||
"title": "Learned Trajectory Annotation",
|
||||
"excerpt":"Learning spatial context representations (left) enables clustering and annotation of trajectories (right). This research addresses the challenge of enabling more intuitive human-robot interaction in shared spaces, particularly focusing on grounding verbal communication in spatial understanding. The work introduces a novel unsupervised learning methodology based on neural autoencoders. The core contribution...","categories": ["research"],
|
||||
"tags": ["geoinformatics","machine-learning","unsupervised-learning","human-robot-interaction","autoencoder"],
|
||||
"url": "/research/learned-trajectory-annotation/",
|
||||
"teaser": "/assets/figures/0_trajectory_reconstruction_teaser.png"
|
||||
},{
|
||||
"title": "DW Editorial Lead",
|
||||
"excerpt":"During my doctoral studies and research tenure at LMU Munich, I led the online editorial team for DIGITALE WELT Magazin. This role, supported by the InnoMi project, involved managing the publication’s digital presence and strategic direction, aiming to effectively bridge scientific research and industry perspectives on digitalization trends. Role: Head...","categories": ["projects"],
|
||||
"tags": [],
|
||||
"url": "/projects/dw/",
|
||||
"teaser": "/assets/images/projects/dw.png"
|
||||
},{
|
||||
"title": "ErLoWa Leak Detection",
|
||||
"excerpt":"In collaboration with Munich’s municipal utility provider, Stadtwerke München (SWM), this project explored the feasibility of using acoustic monitoring for early leak detection in water pipe infrastructure. The primary goal was to develop machine learning models capable of identifying leak-indicating sound patterns within a real-world operational environment. Project: ErLoWa (Erkennung...","categories": ["projects"],
|
||||
"tags": ["acoustic","anomaly-detection","deep-learning","real-world-data","signal-processing"],
|
||||
"url": "/projects/water-networks/",
|
||||
"teaser": "/assets/images/projects/pipe_leak.png"
|
||||
},{
|
||||
"title": "TIMS Seminar",
|
||||
"excerpt":"As part of my teaching responsibilities at the Chair for Mobile and Distributed Systems (LMU Munich), I regularly supervised the “Trends in Mobile and Distributed Systems” (TIMS) seminar series for both Bachelor and Master students. This seminar is designed to introduce students to the process of scientific research and academic...","categories": ["teaching"],
|
||||
"tags": [],
|
||||
"url": "/teaching/seminar-TIMS/",
|
||||
"teaser": "/assets/images/teaching/thesis.png"
|
||||
},{
|
||||
"title": "VTIMS Advanced Seminar",
|
||||
"excerpt":"Complementing the Bachelor-level seminar, I also supervised the “Vertiefte Themen in Mobilen und Verteilten Systemen” (VTIMS) seminar, designed specifically for Master’s students at the LMU Chair for Mobile and Distributed Systems. This advanced seminar aimed to deepen students’ understanding of cutting-edge research topics and further hone their scientific working methodologies....","categories": ["teaching"],
|
||||
"tags": [],
|
||||
"url": "/teaching/seminar-VTIMS/",
|
||||
"teaser": "/assets/images/teaching/thesis_master.png"
|
||||
},{
|
||||
"title": "Neural Self-Replication",
|
||||
"excerpt":"Drawing inspiration from the fundamental process of self-replication in biological systems, this research explores the potential for implementing analogous mechanisms within neural networks. The objective is to develop computational models capable of autonomously reproducing their own structure (specifically, their connection weights), potentially leading to the emergence of complex, adaptive behaviors....","categories": ["research"],
|
||||
"tags": ["neural-networks","artificial-life","complex-systems","self-organization"],
|
||||
"url": "/research/neural-self-replication/",
|
||||
"teaser": "/assets/figures/1_self_replication_pca_space.jpg"
|
||||
},{
|
||||
"title": "Deep Audio Baselines",
|
||||
"excerpt":"This research, presented as part of the Interspeech 2019 Computational Paralinguistics Challenge (ComParE), specifically addresses the Sleepiness Sub-Challenge. We introduced a robust, end-to-end deep learning methodology designed to serve as a strong baseline for audio classification tasks within the paralinguistics domain. The core innovation lies in utilizing a deep neural...","categories": ["research"],
|
||||
"tags": ["deep-learning","audio-classification","paralinguistics","speech-analysis"],
|
||||
"url": "/research/deep-audio-baselines/",
|
||||
"teaser": "/assets/figures/3_deep_neural_baselines_teaser.jpg"
|
||||
},{
|
||||
"title": "OpenMunich Conference Organization",
|
||||
"excerpt":"This event, organized by the LMU Chair for Mobile and Distributed Systems in collaboration with industry partners Accenture and Red Hat, aimed to create a forum for professionals, researchers, and students interested in the latest developments within the open-source ecosystem. The OpenMunich conference series during 2016 until 2019 offered a...","categories": ["projects"],
|
||||
"tags": ["community-engagement"],
|
||||
"url": "/projects/OpenMunich/",
|
||||
"teaser": "/assets/images/projects/openmunich.png"
|
||||
},{
|
||||
"title": "Operating Systems TA",
|
||||
"excerpt":"Following the introductory course on Computer Architecture, I also served as a Teaching Assistant and Tutorial Coordinator for the subsequent “Betriebssysteme” (Operating Systems) lecture at LMU Munich, taught by Prof. Dr. Linnhoff-Popien. This course typically enrolled 300-400 students per semester. My role involved supporting the lecture and managing the associated...","categories": ["teaching"],
|
||||
"tags": [],
|
||||
"url": "/teaching/operating_systems/",
|
||||
"teaser": "/assets/images/teaching/computer_os.png"
|
||||
},{
|
||||
"title": "Soccer Team Vectors",
|
||||
"excerpt":"This research introduces STEVE (Soccer Team Vectors), a novel methodology for learning meaningful, real-valued vector representations (embeddings) for professional soccer teams. The primary goal is to capture intrinsic team characteristics and relationships within a continuous vector space, such that teams with similar playing styles, strengths, or performance levels are positioned...","categories": ["research"],
|
||||
"tags": ["machine-learning","representation-learning","sports-analytics","similarity-search"],
|
||||
"url": "/research/soccer-team-vectors/",
|
||||
"teaser": "/assets/figures/2_steve_algo.jpg"
|
||||
},{
|
||||
"title": "iOS App Development",
|
||||
"excerpt":"Building upon my experience supervising the Android development practical course, I also co-supervised the “iOS Praktikum” at LMU Munich. This hands-on lab course focused on native mobile application development for the Apple iOS platform using the Swift programming language. The course was structured in two main phases: Introductory Phase: Covered...","categories": ["teaching"],
|
||||
"tags": ["teaching","ios","swift","mobile-development","app-development","agile","teamwork"],
|
||||
"url": "/teaching/IOS/",
|
||||
"teaser": "/assets/images/teaching/ios.png"
|
||||
},{
|
||||
"title": "AI-Fusion Safety",
|
||||
"excerpt":"Project Resources GitHub Repo Install via PyPI ReadTheDocs [Altmann et al. 2024] In collaboration with Fraunhofer IKS, the AI-Fusion project addressed the critical challenge of understanding and ensuring safety in multi-agent reinforcement learning (MARL) systems. Emergence, defined as the arising of complex, often unpredictable, system-level dynamics from local interactions between...","categories": ["projects"],
|
||||
"tags": ["multi-agent-systems","reinforcement-learning","safety","emergence","simulation"],
|
||||
"url": "/projects/FIKS/",
|
||||
"teaser": "/assets/images/projects/robot.png"
|
||||
},{
|
||||
"title": "3D Primitive Segmentation",
|
||||
"excerpt":"Overview of the hybrid segmentation and primitive fitting pipeline. This research addresses challenges in accurately segmenting large-scale 3D point clouds into meaningful geometric primitives, specifically spheres, cylinders, and cuboids. Existing methods often struggle with scalability or robustness when faced with diverse shapes and noisy real-world data. We propose a novel...","categories": ["research"],
|
||||
"tags": ["computer-vision","3d-processing","point-clouds","segmentation","deep-learning","genetic-algorithms"],
|
||||
"url": "/research/3d-primitive-segmentation/",
|
||||
"teaser": "/assets/figures/4_point_cloud_segmentation_teaser.jpg"
|
||||
},{
|
||||
"title": "PEOC OOD Detection",
|
||||
"excerpt":"Ensuring the safety and reliability of deep reinforcement learning (RL) agents deployed in real-world environments necessitates the ability to detect when the agent encounters states significantly different from those seen during training (i.e., out-of-distribution or OOD states). This research introduces PEOC (Policy Entropy-based OOD Classifier), a novel and computationally efficient...","categories": ["research"],
|
||||
"tags": ["deep-reinforcement-learning","out-of-distribution-detection","safety","anomaly-detection"],
|
||||
"url": "/research/peoc-ood-detection/",
|
||||
"teaser": "/assets/figures/6_ood_pipeline.jpg"
|
||||
},{
|
||||
"title": "AV Meantime Coverage",
|
||||
"excerpt":"Visualization of estimated service coverage achievable by utilizing parked autonomous vehicles. This research investigates a potential transitional model towards future transportation systems, focusing on privately owned shared autonomous vehicles (SAVs). The central idea, termed “What to do in the Meantime,” explores the feasibility of leveraging these vehicles for ride-sharing services...","categories": ["research"],
|
||||
"tags": ["autonomous-vehicles","shared-mobility","transportation-systems","urban-computing","geoinformatics"],
|
||||
"url": "/research/av-meantime-coverage/",
|
||||
"teaser": "/assets/figures/5_meantime_coverage.jpg"
|
||||
},{
|
||||
"title": "Surgical-Mask Detection",
|
||||
"excerpt":"This study investigates the efficacy of various data augmentation techniques applied directly to mel-spectrogram representations of audio data for improving classification performance. The specific task addressed is the detection of surgical mask usage based on human speech signals, a relevant problem in paralinguistics and audio analysis. We systematically evaluated the...","categories": ["research"],
|
||||
"tags": ["audio-classification","deep-learning","data-augmentation","computer-vision","paralinguistics"],
|
||||
"url": "/research/surgical-mask-detection/",
|
||||
"teaser": "/assets/figures/7_mask_models.jpg"
|
||||
},{
|
||||
"title": "Anomalous Sound Features",
|
||||
"excerpt":"Detecting anomalous sounds, particularly in industrial settings, is crucial for predictive maintenance and safety. This often involves unsupervised or semi-supervised approaches where models learn a representation of ‘normal’ sounds. This research explores the effectiveness of leveraging transfer learning for this task by using pretrained deep neural networks as fixed feature...","categories": ["research"],
|
||||
"tags": ["anomaly-detection","audio-classification","deep-learning","transfer-learning","feature-extraction"],
|
||||
"url": "/research/anomalous-sound-features/",
|
||||
"teaser": "/assets/figures/8_anomalous_sound_teaser.jpg"
|
||||
},{
|
||||
"title": "Sound Anomaly Transfer",
|
||||
"excerpt":"This study investigates an effective approach for acoustic anomaly detection in industrial machinery, focusing on identifying malfunctions through sound analysis. The core methodology leverages transfer learning by repurposing deep neural networks originally trained for large-scale image classification (e.g., on ImageNet) as powerful feature extractors for audio data represented as mel-spectrograms....","categories": ["research"],
|
||||
"tags": ["anomaly-detection","audio-classification","deep-learning","transfer-learning","feature-extraction","computer-vision"],
|
||||
"url": "/research/sound-anomaly-transfer/",
|
||||
"teaser": "/assets/figures/9_image_transfer_sound_teaser.jpg"
|
||||
},{
|
||||
"title": "Acoustic Leak Detection",
|
||||
"excerpt":"Detecting leaks in vast municipal water distribution networks is critical for resource conservation and infrastructure maintenance. This study introduces and evaluates an anomaly detection approach for acoustic leak identification, specifically designed with energy efficiency and ease of deployment as key considerations. The methodology leverages acoustic recordings captured by microphones deployed...","categories": ["research"],
|
||||
"tags": ["anomaly-detection","audio-processing","deep-learning","signal-processing","real-world-application"],
|
||||
"url": "/research/acoustic-leak-detection/",
|
||||
"teaser": "/assets/figures/10_water_networks_teaser.jpg"
|
||||
},{
|
||||
"title": "Primate Vocalization Classification",
|
||||
"excerpt":"Acoustic monitoring offers a powerful, non-invasive tool for wildlife conservation, enabling the study and tracking of animal populations through their vocalizations. This research focuses on improving the automated classification of primate vocalizations, a challenging task due to call variability and environmental noise. We propose a novel deep, recurrent neural network...","categories": ["research"],
|
||||
"tags": ["deep-learning","audio-classification","bioacoustics","conservation-technology","recurrent-neural-networks"],
|
||||
"url": "/research/primate-vocalization-classification/",
|
||||
"teaser": "/assets/figures/11_recurrent_primate_workflow.jpg"
|
||||
},{
|
||||
"title": "Audio Vision Transformer",
|
||||
"excerpt":"This research explores the application of the Vision Transformer (ViT) architecture, originally designed for image processing, to the domain of audio classification by operating on mel-spectrogram representations. The ViT’s attention mechanisms offer a potentially powerful alternative to convolutional approaches for capturing relevant patterns in spectrogram data. Adapting the Vision Transformer...","categories": ["research"],
|
||||
"tags": ["deep-learning","audio-classification","computer-vision","attention-mechanisms","transformers"],
|
||||
"url": "/research/audio-vision-transformer/",
|
||||
"teaser": "/assets/figures/12_vision_transformer_teaser.jpg"
|
||||
},{
|
||||
"title": "Tasked Self-Replication",
|
||||
"excerpt":"Building upon the concept of self-replicating neural networks, this research explores the integration of auxiliary functional goals alongside the primary objective of self-replication. The aim is to create networks that can not only reproduce their own weights but also perform useful computations or interact meaningfully with an environment simultaneously. Analysis...","categories": ["research"],
|
||||
"tags": ["artificial-life","complex-systems","neural-networks","self-organization","multi-task-learning"],
|
||||
"url": "/research/tasked-self-replication/",
|
||||
"teaser": "/assets/figures/13_sr_teaser.jpg"
|
||||
},{
|
||||
"title": "Welcome to Jekyll!",
|
||||
"excerpt":"W. I. P. ","categories": ["blog"],
|
||||
"tags": [],
|
||||
"url": "/blog/wireguard-ui-nat-vps/",
|
||||
"teaser": "/assets/images/newshot_2.jpg"
|
||||
},{
|
||||
"title": "RNN Memory Limits",
|
||||
"excerpt":"Recurrent Neural Networks (RNNs), including variants like Long Short-Term Memory (LSTM) and Gated Recurrent Units (GRU), are designed with the intent to capture temporal dependencies within sequential data. Their internal mechanisms allow information from previous time steps to influence current processing. This research investigates the fundamental memory capacity of these...","categories": ["research"],
|
||||
"tags": ["deep-learning","recurrent-neural-networks","sequence-modeling","theoretical-ml"],
|
||||
"url": "/research/rnn-memory-limits/",
|
||||
"teaser": "/assets/figures/22_rnn_limits.png"
|
||||
},{
|
||||
"title": "RL Anomaly Detection",
|
||||
"excerpt":"Anomaly Detection (AD) is crucial for the safe deployment of Reinforcement Learning (RL) agents, especially in safety-critical applications where encountering unexpected or out-of-distribution situations can lead to catastrophic failures. This work provides a perspective on the state and future directions of AD research specifically tailored for the complexities inherent in...","categories": ["research"],
|
||||
"tags": ["reinforcement-learning","anomaly-detection","safety","lifelong-learning","generalization"],
|
||||
"url": "/research/rl-anomaly-detection/",
|
||||
"teaser": "/assets/figures/14_ad_rl_teaser.jpg"
|
||||
},{
|
||||
"title": "Extended Self-Replication",
|
||||
"excerpt":"Analyzing the lineage and diversity in populations of self-replicating networks. This journal article provides an extended and more in-depth exploration of self-replicating neural networks, building upon earlier foundational work (Gabor et al., 2019). The research further investigates the use of backpropagation-like mechanisms not for typical supervised learning, but as an...","categories": ["research"],
|
||||
"tags": ["artificial-life","complex-systems","neural-networks","self-organization","dynamical-systems"],
|
||||
"url": "/research/extended-self-replication/",
|
||||
"teaser": "/assets/figures/15_sr_journal_teaser.jpg"
|
||||
},{
|
||||
"title": "MSP Android Course",
|
||||
"excerpt":"Over several semesters during my time at LMU Munich, I co-supervised the “Praktikum Mobile und Verteilte Systeme” (MSP), often referred to as the Android development practical course. This intensive lab course provided students with hands-on experience in designing, developing, and testing native applications for the Android platform, primarily using Java...","categories": ["teaching"],
|
||||
"tags": ["teaching","android","java","kotlin","mobile-development","app-development","agile","teamwork"],
|
||||
"url": "/teaching/android/",
|
||||
"teaser": "/assets/images/teaching/android.png"
|
||||
},{
|
||||
"title": "Organism Network Emergence",
|
||||
"excerpt":"This research investigates the transition from simple self-replication to higher levels of organization by exploring how populations of basic, self-replicating neural network units can form “Organism Networks” (ONs) through collaboration and emergent differentiation. Moving beyond the replication of individual networks, the focus shifts to the collective dynamics and functional capabilities...","categories": ["research"],
|
||||
"tags": ["artificial-life","complex-systems","neural-networks","self-organization","emergent-computation"],
|
||||
"url": "/research/organism-network-emergence/",
|
||||
"teaser": "/assets/figures/16_on_teaser.jpg"
|
||||
},{
|
||||
"title": "Voronoi Data Augmentation",
|
||||
"excerpt":"Data augmentation is essential for improving the performance and generalization of Convolutional Neural Networks (CNNs), especially when training data is limited. This research introduces VoronoiPatches (VP), a novel data augmentation algorithm based on the principle of non-linear recombination of image information. Visual example of the VoronoiPatches augmentation applied to an...","categories": ["research"],
|
||||
"tags": ["data-augmentation","computer-vision","deep-learning","convolutional-neural-networks"],
|
||||
"url": "/research/voronoi-data-augmentation/",
|
||||
"teaser": "/assets/figures/17_vp_teaser.jpg"
|
||||
},{
|
||||
"title": "Autoencoder Trajectory Compression",
|
||||
"excerpt":"The proliferation of location-aware mobile devices generates vast amounts of GPS trajectory data, necessitating efficient storage solutions. While various compression techniques aim to reduce data volume, preserving essential spatio-temporal information remains crucial. Schematic of the LSTM Decoder Architecture. This paper introduces a novel approach for compressing and reconstructing GPS trajectories...","categories": ["research"],
|
||||
"tags": ["deep-learning","recurrent-neural-networks","trajectory-analysis","data-compression","geoinformatics"],
|
||||
"url": "/research/autoencoder-trajectory-compression/",
|
||||
"teaser": "/assets/figures/23_trajectory_model.png"
|
||||
},{
|
||||
"title": "Emergent Social Dynamics",
|
||||
"excerpt":"This research extends the study of artificial chemistry systems populated by neural network “particles,” focusing on the emergence of complex behaviors driven by social interaction rather than explicit programming. Building on systems where particles may exhibit self-replication, we introduce interactions based on principles of predictive processing and surprise minimization (akin...","categories": ["research"],
|
||||
"tags": ["artificial-life","complex-systems","neural-networks","self-organization","emergent-behavior","predictive-coding"],
|
||||
"url": "/research/emergent-social-dynamics/",
|
||||
"teaser": "/assets/figures/18_surprised_soup_teaser.jpg"
|
||||
},{
|
||||
"title": "Primate Subsegment Sorting",
|
||||
"excerpt":"Automated acoustic classification plays a vital role in wildlife monitoring and bioacoustics research. This study introduces a sophisticated pre-processing and training strategy to significantly enhance the accuracy of multi-class audio classification, specifically targeting the identification of different primate species from field recordings. A key challenge in bioacoustics is dealing with...","categories": ["research"],
|
||||
"tags": ["bioacoustics","audio-classification","deep-learning","data-labeling","signal-processing"],
|
||||
"url": "/research/primate-subsegment-sorting/",
|
||||
"teaser": "/assets/figures/19_binary_primates_teaser.jpg"
|
||||
},{
|
||||
"title": "LMU DevOps Admin",
|
||||
"excerpt":"During my tenure at the LMU Chair for Mobile and Distributed Systems, alongside my research activities, I assumed responsibility for the ongoing maintenance of the group’s IT infrastructure. This encompassed Linux workstations, Windows Server-based hypervisors, Linux file servers (utilizing ZFS), and core network services. Role: IT Infrastructure & DevOps Lead...","categories": ["projects"],
|
||||
"tags": ["devops","kubernetes","server-administration","infrastructure"],
|
||||
"url": "/projects/server-administration/",
|
||||
"teaser": "/assets/images/projects/arch.png"
|
||||
},{
|
||||
"title": "Aquarium MARL Environment",
|
||||
"excerpt":"The study of complex interactions using Multi-Agent Reinforcement Learning (MARL), particularly predator-prey dynamics, often requires specialized simulation environments. To streamline research and avoid redundant development efforts, we introduce Aquarium: a versatile, open-source MARL environment specifically designed for investigating predator-prey scenarios and related emergent behaviors. Key Features of Aquarium: Framework Integration:...","categories": ["research"],
|
||||
"tags": ["multi-agent-reinforcement-learning","MARL","simulation","emergence","complex-systems"],
|
||||
"url": "/research/aquarium-marl-environment/",
|
||||
"teaser": "/assets/figures/20_aquarium.png"
|
||||
},{
|
||||
"title": "MAS Emergence Safety",
|
||||
"excerpt":"Multi-Agent Systems (MAS), particularly those employing decentralized decision-making based on local information (common in MARL), can exhibit emergent effects. These phenomena, arising from complex interactions, range from minor behavioral quirks to potentially catastrophic system failures, posing significant safety challenges. This research provides a framework for understanding and mitigating undesirable emergence...","categories": ["research"],
|
||||
"tags": ["multi-agent-systems","MARL","safety","emergence","system-specification"],
|
||||
"url": "/research/mas-emergence-safety/",
|
||||
"teaser": "/assets/figures/21_coins_teaser.png"
|
||||
}]
|
||||
3475
_site/assets/js/lunr/lunr.js
Normal file
3475
_site/assets/js/lunr/lunr.js
Normal file
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Load Diff
6
_site/assets/js/lunr/lunr.min.js
vendored
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6
_site/assets/js/lunr/lunr.min.js
vendored
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Reference in New Issue
Block a user