Fix layout, add content, add breadcrumb initial commit

Author: RSTZZZ

_config.yml

@@ -54,7 +54,6 @@ plugins:
   - jekyll-gist
   - jemoji
   - jekyll-include-cache
-  - jekyll-redirect-from
 
 author:
   name   : "Complex Data Lab Member(s)"
@@ -148,6 +147,7 @@ defaults:
       share: true
 
 
+
 category_archive:
   type: liquid
   path: /categories/
@@ -159,4 +159,10 @@ tag_archive:
 collections:
   research_directions:
     output: true
-    permalink: /:path/
+    permalink: /:path/
+
+# Performance
+compress_html:
+  clippings: all
+  # ignore:
+  #   envs: development

_includes/breadcrumbs.html

@@ -0,0 +1,28 @@
+<!-- _includes/breadcrumbs.html -->
+<nav class="breadcrumbs">
+    <ol class="breadcrumb-list" itemscope itemtype="https://schema.org/BreadcrumbList">
+        <!-- Home -->
+        <li class="breadcrumb-item" itemprop="itemListElement" itemscope itemtype="https://schema.org/ListItem">
+            <a href="{{ site.url }}" itemprop="item">
+                <span itemprop="name">Home</span>
+            </a>
+            <meta itemprop="position" content="1" />
+        </li>
+
+        <!-- Previous page (if exists) -->
+        {% if page.parent %}
+        <li class="breadcrumb-item" itemprop="itemListElement" itemscope itemtype="https://schema.org/ListItem">
+            <a href="{{ site.url }}/{{ page.parent | slugify }}" itemprop="item">
+                <span itemprop="name">{{ page.parent }}</span>
+            </a>
+            <meta itemprop="position" content="2" />
+        </li>
+        {% endif %}
+
+        <!-- Current page -->
+        <li class="breadcrumb-item current" itemprop="itemListElement" itemscope itemtype="https://schema.org/ListItem">
+            <span itemprop="name">{{ page.title }}</span>
+            <meta itemprop="position" content="{% if page.parent %}3{% else %}2{% endif %}" />
+        </li>
+    </ol>
+</nav>

_includes/display-publication-links.html

@@ -13,7 +13,7 @@
         <span>Code</span>
     </a>
     {% endif %}
-    
+
     {% if pub.webpage %}
     <a class="publication-ext-link" href="{{ pub.webpage }}" target="_blank">
         <i class="fas fa-link"></i>
@@ -42,4 +42,4 @@
     </a>
     {% endif %}
 
-</div>
+</div>

_includes/head/custom.html

@@ -63,4 +63,23 @@
     updateNodesRel(theme);
 
 </script>
+
+<script type="text/javascript">
+    document.addEventListener('DOMContentLoaded', function () {
+        // Get the current URL path
+        const path = window.location.pathname;
+        const pathSegments = path.split('/').filter(segment => segment);
+
+        // Update breadcrumb positions dynamically
+        const breadcrumbItems = document.querySelectorAll('.breadcrumb-item');
+        breadcrumbItems.forEach((item, index) => {
+            const position = item.querySelector('[itemprop="position"]');
+            if (position) {
+                position.content = (index + 1).toString();
+            }
+        });
+    });
+</script>
+
+
 {% endif %}

_includes/research-directions.html

@@ -12,23 +12,18 @@
   <div class="research-directions-item">
     <a href="{{ r.url | relative_url }}" title="{{r.title}}">
 
-      <div class="direction-image" style="background-image: url('{{ r.header.overlay_image | relative_url }}');
+      <div class="direction-image" style="
+      background-image: 
       {% if r.header.overlay_filter %}
-        background-image: {{ r.header.overlay_filter }}, url('{{ r.header.overlay_image | relative_url }}');
-      {% endif %}">
-        <div class="direction-header">
-          {% if r.logo_image_path %}
-          <div class="research-directions-logo">
-            {% if r.logo_dark_image_path %}
-            <img loading="lazy" src="{{ r.logo_image_path | relative_url }}" class="theme-image light-image"
-              alt="{% if r.alt %}{{ r.alt }}{% endif %}">
-            <img loading="lazy" src="{{ r.logo_dark_image_path | relative_url }}" class="theme-image dark-image"
-              alt="{% if r.alt %}{{ r.alt }}{% endif %}">
-            {% else %}
-            <img loading="lazy" src="{{ r.logo_image_path | relative_url }}" alt="{{ r.title }}">
-            {% endif %}
-          </div>
-          {% endif %}
+      {{ r.header.overlay_filter }},
+      {% endif %}
+      url('{{ r.header.overlay_image | relative_url }}');
+      {% if r.header.overlay_size %} 
+      background-size: {{r.header.overlay_size }}; 
+      {% endif %}
+      ">
+        <div class="direction-title">
+
           <h2>{{ r.title }}</h2>
         </div>
         {% if r.one-liner %}

_includes/sub_research-directions.html

@@ -18,21 +18,29 @@
 {% endif %}
 {% endif %}
 
-<div class="sub-research-directions {{ gallery_layout }} {{ include.class }}">
+<div class="sub-research-directions__wrapper  {{ gallery_layout }}">
     {% for direction in sorted_directions %}
-    <div class="direction-item">
+    <div class="sub-research-directions-item">
         <a href="{{ direction.url | relative_url }}" title="{{ direction.title }}">
-            <div class="direction-image" style="
+            <div class="sub-direction-image" style="
                    background-image:
                    {% if direction.header.overlay_filter %} {{ direction.header.overlay_filter }},{% endif %}
                    url('{{ direction.header.overlay_image | relative_url }}');
                    {% if direction.header.overlay_size %}
                    background-size: {{ direction.header.overlay_size }};
                    {% endif %}
                    ">
-                <div class="direction-title">
+
+
+                <div class="sub-direction-title">
                     <h2>{{ direction.title }}</h2>
                 </div>
+                {% if direction.one-liner %}
+                <div class="sub-direction-excerpt">
+                    {{ direction.one-liner | markdownify }}
+                </div>
+                {% endif %}
+
             </div>
         </a>
     </div>

_posts/papers/2020-10-03-2010.01408.md

@@ -10,7 +10,8 @@ author: Shenyang Huang
 categories: 
 - Publications
 - TGA
-excerpt: ""
+excerpt: In this paper, we introduce a significant enhancement to the standard SEIR (Susceptible, Exposed, Infectious, Recovered) epidemiological model by integrating dynamic flight networks to better capture the mobility of populations, particularly in the context of disease spread like COVID-19. The authors propose a modified model called Flight-SEIR, which accounts for the movement of individuals through air travel, thereby estimating imported cases based on air traffic volume and test positive rates. This modification allows for more accurate modeling of disease transmission between populations, enabling early detection of outbreaks, more precise estimation of the reproduction number, and better evaluation of the impact of travel restrictions. By incorporating real-world flight data, Flight-SEIR provides a more realistic and comprehensive approach to epidemiological modeling, crucial for navigating pandemics in an interconnected world.
+
 
 ---
 
@@ -22,4 +23,4 @@ excerpt: ""
 
 ## Abstract
 
-None
+Current efforts of modelling COVID-19 are often based on the standard compartmental models such as SEIR and their variations. As pre-symptomatic and asymptomatic cases can spread the disease between populations through travel, it is important to incorporate mobility between populations into the epidemiological modelling. In this work, we propose to modify the commonly-used SEIR model to account for the dynamic flight network, by estimating the imported cases based on the air traffic volume as well as the test positive rate at the source. This modification, called Flight-SEIR, can potentially enable 1). early detection of outbreaks due to imported pre-symptomatic and asymptomatic cases, 2). more accurate estimation of the reproduction number and 3). evaluation of the impact of travel restrictions and the implications of lifting these measures. The proposed Flight-SEIR is essential in navigating through this pandemic and the next ones, given how interconnected our world has become.

_posts/papers/2021-01-01-10.1007-978-3-030-75762-5_3.md

@@ -11,7 +11,7 @@ author: Farimah Poursafaei
 categories: 
 - Publications
 - TGA
-excerpt: ""
+excerpt: SigTran is an efficient graph-based method for identifying illicit nodes in blockchain networks. SigTran constructs a graph from blockchain transaction records, representing nodes based on their structural and transactional characteristics to differentiate between legitimate and illicit activities. This method is versatile and can be applied to various blockchain networks. 
 
 ---
 
@@ -23,4 +23,4 @@ excerpt: ""
 
 ## Abstract
 
-None
+Cryptocurrency networks have evolved into multi-billion-dollar havens for a variety of disputable financial activities, including phishing, ponzi schemes, money-laundering, and ransomware. In this paper, we propose an efficient graph-based method, SigTran, for detecting illicit nodes on blockchain networks. SigTran first generates a graph based on the transaction records from blockchain. It then represents the nodes based on their structural and transactional characteristics. These node representations accurately differentiate nodes involved in illicit activities. SigTran is generic and can be applied to records extracted from different networks. SigTran achieves an F_1 score of 0.92 on Bitcoin and 0.94 on Ethereum, which outperforms the state-of-the-art performance on these benchmarks obtained by much more complex, platform-dependent models.

_posts/papers/2021-04-01-10.1109-ICDE51399.2021.00101.md

@@ -6,7 +6,12 @@ tags:
 - IEEE International Conference on Data Engineering
 link: https://doi.org/10.1109/ICDE51399.2021.00101
 author: Sacha Levy
-categories: Publications
+categories:
+- Publications
+- online-crime
+
+excerpt: INFOSHIELD is a scalable, parameter-free, and interpretable tool for detecting near-duplicate document clusters, with applications in human trafficking detection, spam-bot identification, and plagiarism. It highlights common phrases, detects unique "slots," and selects representative documents for clear visualization. INFOSHIELD is language-independent, outperforming domain-specific methods across datasets in multiple languages. It can process 4 million documents in just 8 hours on standard hardware.
+
 
 ---
 

_posts/papers/2021-04-14-2104.06952.md

@@ -9,7 +9,8 @@ author: Kellin Pelrine
 categories: 
 - Publications
 - information-integrity
-excerpt: ""
+excerpt: While many sophisticated detection models have been proposed in the literature, they were often compared with older NLP baselines such as SVMs, CNNs, and LSTMs. We showed that with basic fine-tuning, BERT-type language models were competitive with and could even significantly outperform state-of-the-art methods of the time. We further studied a comprehensive set of benchmark datasets, and discuss potential data leakage and the need for careful design of the experiments and understanding of datasets to account for confounding variables. 
+
 
 ---