EmbedFuzz: High Speed Fuzzing Through Transplantation

Autor: Hofhammer, Florian, Wang, Qinying, Bhattacharyya, Atri, Salehi, Majid, Crispo, Bruno, Egele, Manuel, Payer, Mathias, Busch, Marcel

Dynamic analysis and especially fuzzing are challenging tasks for embedded firmware running on modern low-end Microcontroller Units (MCUs) due to performance overheads from instruction emulation, the difficulty of emulating the vast space of availabl

Externí odkaz: http://arxiv.org/abs/2412.12746

Could ChatGPT get an Engineering Degree? Evaluating Higher Education Vulnerability to AI Assistants

Autor: Borges, Beatriz, Foroutan, Negar, Bayazit, Deniz, Sotnikova, Anna, Montariol, Syrielle, Nazaretzky, Tanya, Banaei, Mohammadreza, Sakhaeirad, Alireza, Servant, Philippe, Neshaei, Seyed Parsa, Frej, Jibril, Romanou, Angelika, Weiss, Gail, Mamooler, Sepideh, Chen, Zeming, Fan, Simin, Gao, Silin, Ismayilzada, Mete, Paul, Debjit, Schöpfer, Alexandre, Janchevski, Andrej, Tiede, Anja, Linden, Clarence, Troiani, Emanuele, Salvi, Francesco, Behrens, Freya, Orsi, Giacomo, Piccioli, Giovanni, Sevel, Hadrien, Coulon, Louis, Pineros-Rodriguez, Manuela, Bonnassies, Marin, Hellich, Pierre, van Gerwen, Puck, Gambhir, Sankalp, Pirelli, Solal, Blanchard, Thomas, Callens, Timothée, Aoun, Toni Abi, Alonso, Yannick Calvino, Cho, Yuri, Chiappa, Alberto, Sclocchi, Antonio, Bruno, Étienne, Hofhammer, Florian, Pescia, Gabriel, Rizk, Geovani, Dadi, Leello, Stoffl, Lucas, Ribeiro, Manoel Horta, Bovel, Matthieu, Pan, Yueyang, Radenovic, Aleksandra, Alahi, Alexandre, Mathis, Alexander, Bitbol, Anne-Florence, Faltings, Boi, Hébert, Cécile, Tuia, Devis, Maréchal, François, Candea, George, Carleo, Giuseppe, Chappelier, Jean-Cédric, Flammarion, Nicolas, Fürbringer, Jean-Marie, Pellet, Jean-Philippe, Aberer, Karl, Zdeborová, Lenka, Salathé, Marcel, Jaggi, Martin, Rajman, Martin, Payer, Mathias, Wyart, Matthieu, Gastpar, Michael, Ceriotti, Michele, Svensson, Ola, Lévêque, Olivier, Ienne, Paolo, Guerraoui, Rachid, West, Robert, Kashyap, Sanidhya, Piazza, Valerio, Simanis, Viesturs, Kuncak, Viktor, Cevher, Volkan, Schwaller, Philippe, Friedli, Sacha, Jermann, Patrick, Käser, Tanja, Bosselut, Antoine

Publikováno v: PNAS (2024) Vol. 121 | No. 49

AI assistants are being increasingly used by students enrolled in higher education institutions. While these tools provide opportunities for improved teaching and education, they also pose significant challenges for assessment and learning outcomes.

Externí odkaz: http://arxiv.org/abs/2408.11841

A Survey on Quality Metrics for Text-to-Image Models

Autor: Hartwig, Sebastian, Engel, Dominik, Sick, Leon, Kniesel, Hannah, Payer, Tristan, Poonam, Poonam, Glöckler, Michael, Bäuerle, Alex, Ropinski, Timo

Recent AI-based text-to-image models not only excel at generating realistic images, they also give designers more and more fine-grained control over the image content. Consequently, these approaches have gathered increased attention within the comput

Externí odkaz: http://arxiv.org/abs/2403.11821

Top of the Heap: Efficient Memory Error Protection of Safe Heap Objects

Autor: Huang, Kaiming, Payer, Mathias, Qian, Zhiyun, Sampson, Jack, Tan, Gang, Jaeger, Trent

Heap memory errors remain a major source of software vulnerabilities. Existing memory safety defenses aim at protecting all objects, resulting in high performance cost and incomplete protection. Instead, we propose an approach that accurately identif

Externí odkaz: http://arxiv.org/abs/2310.06397

Self-Supervised Pre-Training with Contrastive and Masked Autoencoder Methods for Dealing with Small Datasets in Deep Learning for Medical Imaging

Autor: Wolf, Daniel, Payer, Tristan, Lisson, Catharina Silvia, Lisson, Christoph Gerhard, Beer, Meinrad, Götz, Michael, Ropinski, Timo

Deep learning in medical imaging has the potential to minimize the risk of diagnostic errors, reduce radiologist workload, and accelerate diagnosis. Training such deep learning models requires large and accurate datasets, with annotations for all tra

Externí odkaz: http://arxiv.org/abs/2308.06534