deploy: 3021e9c

Author: cmihaigabriel

bib/index.html

@@ -207,11 +207,14 @@ <h2 class="subtitle">Bibliography</h2>
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-	<p>This bibliography contains a list of the papers that have been published using data from the Multimedia Evaluation Benchmark (MediaEval). It includes not only the papers from the proceedings of the yearly MediaEval workshop, but also conference and journal papers that have been published, as well as some theses. So far, we found 1193 papers that use data from MediaEval.</p>
+	<p>This bibliography contains a list of the papers that have been published using data from the Multimedia Evaluation Benchmark (MediaEval). It includes not only the papers from the proceedings of the yearly MediaEval workshop, but also conference and journal papers that have been published, as well as some theses. So far, we found 1198 papers that use data from MediaEval.</p>
 
 <p>If you don’t see your paper here and would like to have it included, please get in touch with Mihai Gabriel Constantin: mihai.constantin84 (at) upb.ro.</p>
 
-<ul class="bibliography"><li><span id="app15042169">Liu, D., &amp; Zhou, Z. (2025). AdaptiveConv2d: A Novel Convolutional Module for Medical Image Segmentation. <i>Applied Sciences</i>, <i>15</i>(4). https://doi.org/10.3390/app15042169</span></li>
+<ul class="bibliography"><li><span id="Byeon02102025">Byeon, H., AlMahadin, G., Bsoul, Q., Quraishi, A., Soni, M., Khan, S. R., &amp; Shabaz, M. (2025). Ambiguity learning and data correlation in multi-cross modal cyber-physical systems for detecting fake information. <i>Cyber-Physical Systems</i>, <i>11</i>(4), 488–507. https://doi.org/10.1080/23335777.2025.2467638</span></li>
+<li><span id="fakhfakh2025personalized">Fakhfakh, R., Feki, G., &amp; Amar, C. B. (2025). Personalized Open-Vocabulary Image Retrieval via Semantic and SRSiM-Based Social Features. <i>Informatica</i>, <i>49</i>(9).</span></li>
+<li><span id="10908402">Ojo, A. O., Najar, F., Zamzami, N., Himdi, H. T., &amp; Bouguila, N. (2025). SmoothDectector: A Smoothed Dirichlet Multimodal Approach for Combating Fake News on Social Media. <i>IEEE Access</i>, <i>13</i>, 39289–39305. https://doi.org/10.1109/ACCESS.2025.3546876</span></li>
+<li><span id="app15042169">Liu, D., &amp; Zhou, Z. (2025). AdaptiveConv2d: A Novel Convolutional Module for Medical Image Segmentation. <i>Applied Sciences</i>, <i>15</i>(4). https://doi.org/10.3390/app15042169</span></li>
 <li><span id="harini2025long">Harini, S. I., Singh, S., Singla, Y. K., Bhattacharyya, A., Baths, V., Chen, C., Shah, R. R., &amp; Krishnamurthy, B. (2025). Long-Term Ad Memorability: Understanding &amp; Generating Memorable Ads. <i>2025 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)</i>, 5707–5718.</span></li>
 <li><span id="watcharasupat2025uncertainty">Watcharasupat, K. N., Ding, Y., Ma, T. A., Seshadri, P., &amp; Lerch, A. (2025). Uncertainty Estimation in the Real World: A Study on Music Emotion Recognition. <i>European Conference on Information Retrieval</i>, 218–232.</span></li>
 <li><span id="kumar2025seeing">Kumar, P., Khandelwal, E., Tapaswi, M., &amp; Sreekumar, V. (2025). Seeing Eye to AI: Comparing human gaze and model attention in video memorability. <i>2025 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)</i>, 2082–2091.</span></li>
@@ -222,6 +225,8 @@ <h2 class="subtitle">Bibliography</h2>
 <li><span id="fong2025theory">Fong, H., Kumar, V., &amp; Sudhir, K. (2025). A theory-based explainable deep learning architecture for music emotion. <i>Marketing Science</i>, <i>44</i>(1), 196–219.</span></li>
 <li><span id="wang2025fake">Wang, X., Meng, J., Zhao, D., Meng, X., &amp; Sun, H. (2025). Fake news detection based on multi-modal domain adaptation. <i>Neural Computing and Applications</i>, <i>37</i>(7), 5781–5793.</span></li>
 <li><span id="qiang2025mle">Qiang, R., Zhuang, Y., Singh, A., Liang, P., Zhang, C., Yang, S., &amp; Dai, B. (2025). MLE-Smith: Scaling MLE Tasks with Automated Multi-Agent Pipeline. <i>ArXiv Preprint ArXiv:2510.07307</i>.</span></li>
+<li><span id="ghasemizade2024developing">Ghasemizade, M., &amp; Onaolapo, J. (2024). Developing a hierarchical model for unraveling conspiracy theories. <i>EPJ Data Science</i>, <i>13</i>(1), 31.</span></li>
+<li><span id="deshmukh2024fabrication">Deshmukh, V. J., &amp; Ambhaikar, A. (2024). Fabrication Classifiation in Visual Tweets by Using Machine Learning with Deep Features. <i>2024 IEEE International Students’ Conference on Electrical, Electronics and Computer Science (SCEECS)</i>, 1–5.</span></li>
 <li><span id="ekbal2024recent">Ekbal, A., &amp; Kumari, R. (2024). Recent Advancements in Misinformation Detection. In <i>Dive into Misinformation Detection: From Unimodal to Multimodal and Multilingual Misinformation Detection</i> (pp. 17–39). Springer.</span></li>
 <li><span id="10862710">Juma-Ang, C. S., Torre, N. A., &amp; Bandalan, C. (2024). Music Emotion Recognition Using Ensemble Learning. <i>2024 IEEE International Conference on Communication, Networks and Satellite (COMNETSAT)</i>, 289–295. https://doi.org/10.1109/COMNETSAT63286.2024.10862710</span></li>
 <li><span id="ekbal2024novelty">Ekbal, A., &amp; Kumari, R. (2024). Novelty and Emotion in Multimodal Misinformation Detection. In <i>Dive into Misinformation Detection: From Unimodal to Multimodal and Multilingual Misinformation Detection</i> (pp. 109–128). Springer.</span></li>

editions/2020/index.html

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editions/2020/tasks/fakenews/index.html

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editions/2020/tasks/floodmultimedia/index.html

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editions/2020/tasks/gamestory/index.html

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editions/2020/tasks/lifelogging/index.html

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editions/2020/tasks/medico/index.html

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editions/2020/tasks/memorability/index.html

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