Search Results for author:
Found 51 papers, 24 papers with code
GRhOOT: Ontology of Rhetorical Figures in German
GRhOOT, the German RhetOrical OnTology, is a domain ontology of 110 rhetorical figures in the German language.
Bridging the Semantic Gap in Virtual Machine Introspection and Forensic Memory Analysis
We also test our method on complete physical memory dumps to showcase the effectiveness of the engineered features.
WebFAQ: A Multilingual Collection of Natural Q&A Datasets for Dense Retrieval
These datasets are carefully curated through refined filtering and near-duplicate detection, yielding high-quality resources for training and evaluating multilingual dense retrieval models.
Malware Detection based on API calls
We also empirically show that we only need a subset of the function call sequence, specifically calls to the ntdll. dll library, to identify malware.
On the Suitability of pre-trained foundational LLMs for Analysis in German Legal Education
We show that current open-source foundational LLMs possess instruction capability and German legal background knowledge that is sufficient for some legal analysis in an educational context.
Enhancing Rhetorical Figure Annotation: An Ontology-Based Web Application with RAG Integration
To overcome this issue, we develop a web application called "Find your Figure" that facilitates the identification and annotation of German rhetorical figures.
Krony-PT: GPT2 compressed with Kronecker Products
We introduce Krony-PT, a compression technique of GPT2 \citep{radford2019language} based on Kronecker Products.
SUDS: A Strategy for Unsupervised Drift Sampling
We also introduce the Harmonized Annotated Data Accuracy Metric (HADAM), a metric that evaluates classifier performance in relation to the quantity of annotated data required to achieve the stated performance, thereby taking into account the difficulty of acquiring labeled data.
Towards an Improved Metric for Evaluating Disentangled Representations
Disentangled representation learning plays a pivotal role in making representations controllable, interpretable and transferable.
Mixture of Modular Experts: Distilling Knowledge from a Multilingual Teacher into Specialized Modular Language Models
Evaluations of modular MoE architectures revealed that Pre-trained Language Experts (PLE) and Joint Expert Embedding Training (JEET) performed similarly, while the MoE with Common Expert (MoE-CE) setup showed slightly lower performance.
PersonaRAG: Enhancing Retrieval-Augmented Generation Systems with User-Centric Agents
Large Language Models (LLMs) struggle with generating reliable outputs due to outdated knowledge and hallucinations.
Beyond Benchmarks: Evaluating Embedding Model Similarity for Retrieval Augmented Generation Systems
Through our experiments we identify clusters of models corresponding to model families, but interestingly, also some inter-family clusters.
DriftGAN: Using historical data for Unsupervised Recurring Drift Detection
In real-world applications, input data distributions are rarely static over a period of time, a phenomenon known as concept drift.
Challenges and Considerations in Annotating Legal Data: A Comprehensive Overview
This paper provides an expanded view of these challenges and aims to offer a foundational understanding and guidance for researchers and professionals engaged in legal data annotation projects.
Computational Approaches to the Detection of Lesser-Known Rhetorical Figures: A Systematic Survey and Research Challenges
Rhetorical figures play a major role in our everyday communication as they make text more interesting, more memorable, or more persuasive.
Efficient NAS with FaDE on Hierarchical Spaces
Our experiments show that firstly, FaDE-ranks on finite regions of the search space correlate with corresponding architecture performances and secondly, the ranks can empower a pseudo-gradient evolutionary search on the complete neural architecture search space.
A Survey of Web Content Control for Generative AI
The groundbreaking advancements around generative AI have recently caused a wave of concern culminating in a row of lawsuits, including high-profile actions against Stability AI and OpenAI.
LLMs in the Loop: Leveraging Large Language Model Annotations for Active Learning in Low-Resource Languages
To address this gap, we propose leveraging the potential of LLMs in the active learning loop for data annotation.
Towards Measuring Representational Similarity of Large Language Models
Understanding the similarity of the numerous released large language models (LLMs) has many uses, e. g., simplifying model selection, detecting illegal model reuse, and advancing our understanding of what makes LLMs perform well.
Is GPT-4 a reliable rater? Evaluating Consistency in GPT-4 Text Ratings
Statistical analysis was conducted in order to learn more about the interrater reliability, consistency of the ratings across iterations and the correlation between ratings in terms of content and style.
GRAN is superior to GraphRNN: node orderings, kernel- and graph embeddings-based metrics for graph generators
We use these metrics to compare GraphRNN and GRAN, two well-known generative models for graphs, and unveil the influence of node orderings.
Knowledge distillation with Segment Anything (SAM) model for Planetary Geological Mapping
Planetary science research involves analysing vast amounts of remote sensing data, which are often costly and time-consuming to annotate and process.
Technical Report: Impact of Position Bias on Language Models in Token Classification
Therefore, we conduct an in-depth evaluation of the impact of position bias on the performance of LMs when fine-tuned on token classification benchmarks.
German BERT Model for Legal Named Entity Recognition
Even though there is much research done on NER using BERT and other popular language models, the same is not explored in detail when it comes to Legal NLP or Legal Tech.
SmartKex: Machine Learning Assisted SSH Keys Extraction From The Heap Dump
A commonly used method in digital forensics is to extract data from the main memory of a digital device.
The Importance of Future Information in Credit Card Fraud Detection
We believe that future works on this new paradigm will have a significant impact on the detection of compromised cards.
deepstruct -- linking deep learning and graph theory
deepstruct connects deep learning models and graph theory such that different graph structures can be imposed on neural networks or graph structures can be extracted from trained neural network models.
Experiments on Properties of Hidden Structures of Sparse Neural Networks
Sparsity in the structure of Neural Networks can lead to less energy consumption, less memory usage, faster computation times on convenient hardware, and automated machine learning.
Conditional Generative Adversarial Networks for Speed Control in Trajectory Simulation
During prediction, CSG forecasts future speed from latent space and conditions its generation based on it.
nlpUP at SemEval-2020 Task 12 : A Blazing Fast System for Offensive Language Detection
In this paper, we introduce our submission for the SemEval Task 12, sub-tasks A and B for offensive language identification and categorization in English tweets.
NLP\_Passau at SemEval-2020 Task 12: Multilingual Neural Network for Offensive Language Detection in English, Danish and Turkish
This paper describes a neural network (NN) model that was used for participating in the OffensEval, Task 12 of the SemEval 2020 workshop.
GruPaTo at SemEval-2020 Task 12: Retraining mBERT on Social Media and Fine-tuned Offensive Language Models
We introduce an approach to multilingual Offensive Language Detection based on the mBERT transformer model.
Lessons Learned from the 1st ARIEL Machine Learning Challenge: Correcting Transiting Exoplanet Light Curves for Stellar Spots
For instance, the most prolific method for detecting exoplanets and inferring several of their characteristics, transit photometry, is very sensitive to the presence of stellar spots.
HateBERT: Retraining BERT for Abusive Language Detection in English
In this paper, we introduce HateBERT, a re-trained BERT model for abusive language detection in English.
Ranked #1 on
Hate Speech Detection
on AbusEval
ADSAGE: Anomaly Detection in Sequences of Attributed Graph Edges applied to insider threat detection at fine-grained level
We evaluate ADSAGE on authentication, email traffic and web browsing logs from the CERT insider threat datasets, as well as on real-world authentication events.
DeepGG: a Deep Graph Generator
Learning distributions of graphs can be used for automatic drug discovery, molecular design, complex network analysis, and much more.
Ranked #1 on
Graph Embedding
on Barabasi-Albert
Language Proficiency Scoring
The Common European Framework of Reference (CEFR) provides generic guidelines for the evaluation of language proficiency.
I Feel Offended, Don't Be Abusive! Implicit/Explicit Messages in Offensive and Abusive Language
However, there is a lack of data sets that take into account the degree of explicitness.
Investigating Extensions to Random Walk Based Graph Embedding
Graph embedding has recently gained momentum in the research community, in particular after the introduction of random walk and neural network based approaches.
Predicting event attendance exploring social influence
In this paper, we propose to model the social influence of friends on event attendance.
Global and Local Feature Learning for Ego-Network Analysis
This social network can be efficiently analyzed after learning representations of the ego and its alters in a low-dimensional, real vector space.
Shortest path distance approximation using deep learning techniques
Computing shortest path distances between nodes lies at the heart of many graph algorithms and applications.
Competitive Influence Maximization: Integrating Budget Allocation and Seed Selection
In this work, we integrate these two lines of research and propose a new scenario where competition happens in two phases.
Social and Information Networks Computer Science and Game Theory
Parallel Total Variation Distance Estimation with Neural Networks for Merging Over-Clusterings
We consider the initial situation where a dataset has been over-partitioned into $k$ clusters and seek a domain independent way to merge those initial clusters.
Policy Learning for Malaria Control
In this report, we introduce the progress to learn the policy for Malaria Control as a Reinforcement Learning problem in the KDD Cup Challenge 2019 and propose diverse solutions to deal with the limited observations problem.
Structural Analysis of Sparse Neural Networks
Sparse Neural Networks regained attention due to their potential for mathematical and computational advantages.
Towards automated feature engineering for credit card fraud detection using multi-perspective HMMs
Our multiple perspectives HMM-based approach offers automated feature engineering to model temporal correlations so as to improve the effectiveness of the classification task and allows for an increase in the detection of fraudulent transactions when combined with the state of the art expert based feature engineering strategy for credit card fraud detection.
Dataset shift quantification for credit card fraud detection
This phenomenon is named dataset shift or concept drift in the domain of fraud detection.
nlpUP at SemEval-2019 Task 6: A Deep Neural Language Model for Offensive Language Detection
In addition, we evaluate our approach on a different dataset and show that our model is capable of detecting online aggressiveness in both English and German tweets.
Multiple perspectives HMM-based feature engineering for credit card fraud detection
In this article, we model a sequence of credit card transactions from three different perspectives, namely (i) does the sequence contain a Fraud?
Analysing Neural Network Topologies: a Game Theoretic Approach
In previous work, heuristics based on using the weight distribution of a neuron as contribution measure have shown some success, but do not provide a proper theoretical understanding.
