34 citations · 62 across the 10 of their papers we have counts for
10 papers
Jina CLIP: Your CLIP Model Is Also Your Text Retriever
Andreas Koukounas, Georgios Mastrapas, Michael Günther +11
Contrastive Language-Image Pretraining (CLIP) is widely used to train models to align images and texts in a common embedding space by mapping them to fixed-sized vectors. These mod…
Multi-Task Contrastive Learning for 8192-Token Bilingual Text Embeddings
Isabelle Mohr, Markus Krimmel, Saba Sturua +16
We introduce a novel suite of state-of-the-art bilingual text embedding models that are designed to support English and another target language. These models are capable of process…
Compositional Fuzzing Aided by Targeted Symbolic Execution
Saahil Ognawala, Fabian Kilger, Alexander Pretschner
Guided fuzzing has, in recent years, been able to uncover many new vulnerabilities in real-world software due to its fast input mutation strategies guided by path-coverage. However…
Automatically Assessing Vulnerabilities Discovered by Compositional Analysis
Saahil Ognawala, Ricardo Nales Amato, Alexander Pretschner +1
Testing is the most widely employed method to find vulnerabilities in real-world software programs. Compositional analysis, based on symbolic execution, is an automated testing met…
Reviewing KLEE's Sonar-Search Strategy in Context of Greybox Fuzzing
Saahil Ognawala, Alexander Pretschner, Thomas Hutzelmann +2
Automatic test-case generation techniques of symbolic execution and fuzzing are the most widely used methods to discover vulnerabilities in, both, academia and industry. However, b…
An Exploratory Survey of Hybrid Testing Techniques Involving Symbolic Execution and Fuzzing
Saahil Ognawala, Ana Petrovska, Kristian Beckers
Recent efforts in practical symbolic execution have successfully mitigated the path-explosion problem to some extent with search-based heuristics and compositional approaches. Simi…
Improving Function Coverage with Munch: A Hybrid Fuzzing and Directed Symbolic Execution Approach
Saahil Ognawala, Thomas Hutzelmann, Eirini Psallida +1
Fuzzing and symbolic execution are popular techniques for finding vulnerabilities and generating test-cases for programs. Fuzzing, a blackbox method that mutates seed input values,…
Where do we stand in requirements engineering improvement today? First results from a mapping study
Daniel Méndez Fernández, Saahil Ognawala, Stefan Wagner +1
Context: Requirements engineering process improvement (REPI) approaches have gained much attention in research and practice. Goal: So far, there is no comprehensive view on the res…
ML-based tactile sensor calibration: A universal approach
Maximilian Karl, Artur Lohrer, Dhananjay Shah +5
We study the responses of two tactile sensors, the fingertip sensor from the iCub and the BioTac under different external stimuli. The question of interest is to which degree both…
Regularizing Recurrent Networks - On Injected Noise and Norm-based Methods
Saahil Ognawala, Justin Bayer
Advancements in parallel processing have lead to a surge in multilayer perceptrons' (MLP) applications and deep learning in the past decades. Recurrent Neural Networks (RNNs) give…