Inst. Softwaretechnik und Theoretische Informatik

371 Items

Recent Submissions
Laser logic state images of masked AES implementations from registers on a Cyclone IV FPGA

Krachenfels, Thilo (2020-09-03)

This repository contains images (in Tiff format) that were captured using the Phemos-1000 failure analysis microscope with the LLSI (Laser Logic State Imaging) technique. Each image contains 16 bits stored in the registers of a Xilinx Cyclone IV FPGA. Furthermore, the repository contains scripts (in Matlab programming language) for extracting the bit values from the images. This data package is...

Dari Dataset for Coreference Resolution

Zia, Ghezal Ahmad Jan (2020-09-08)

DariCoref, a Dari corpus annotated for anaphoric relations, where all documents are collected from Dari VOA and Azadi Radio. The annotation scheme follows the OntoNotes and WikiCoref. Each markable annotated with coreference type (Identical, Attributive, and Copular), and mention type (Named Entity, Noun Phrase, and Pronominal). Since this is the first annotation efforts concentrate on very spe...

Towards secure 4G and 5G access network protocols

Shaik, Altaf (2020)

The security architecture of 2G and 3G mobile networks has been dramatically improved to accommodate 4G (Fourth Generation, a.k.a Long Term Evolution (LTE)) security requirements. As generations evolve, security improvements address previously known vulnerabilities, esp. in terms of user privacy. Thus, there have been substantial efforts to protect user plane traffic by using robust encryption ...

The neurophysiology of EEG and the physics of the head

Miklody, Daniel (2020)

Neuroscientific research using Electroencephalography is one of the most important tools for understanding human brain function and dysfunction. Not many other methods can non-invasively and directly access neural activity with millisecond precision. While basic event related modulations of brain activity can easily be replicated, the spatial resolution remains poor. Thus, complex or higher bra...

Exploring density functional subspaces with genetic algorithms

Gastegger, Michael ; González, Leticia ; Marquetand, Philipp (2018-12-14)

We use a genetic algorithm to explore the subspace of combination and parametrization patterns spanned by a set of popular exchange and correlation functional approximations. Using the well-balanced GMTKN30 benchmark database to guide the evolutionary process, we find that the genetic algorithm is able to recover variants of several popular generalized gradient approximation functionals and hyb...

Combining SchNet and SHARC: The SchNarc Machine Learning Approach for Excited-State Dynamics

Westermayr, Julia ; Gastegger, Michael ; Marquetand, Philipp (2020-04-20)

In recent years, deep learning has become a part of our everyday life and is revolutionizing quantum chemistry as well. In this work, we show how deep learning can be used to advance the research field of photochemistry by learning all important properties—multiple energies, forces, and different couplings—for photodynamics simulations. We simplify such simulations substantially by (i) a phase-...

Machine learning enables long time scale molecular photodynamics simulations

Westermayr, Julia ; Gastegger, Michael ; Menger, Maximilian F. S. J. ; Mai, Sebastian ; González, Leticia ; Marquetand, Philipp (2019-08-05)

Photo-induced processes are fundamental in nature but accurate simulations of their dynamics are seriously limited by the cost of the underlying quantum chemical calculations, hampering their application for long time scales. Here we introduce a method based on machine learning to overcome this bottleneck and enable accurate photodynamics on nanosecond time scales, which are otherwise out of re...

Times Are Changing: Investigating the Pace of Language Change in Diachronic Word Embeddings

Brandl, Stephanie ; Lassner, David (2019-08-02)

We propose Word Embedding Networks, a novel method that is able to learn word embeddings of individual data slices while simultaneously aligning and ordering them without feeding temporal information a priori to the model. This gives us the opportunity to analyse the dynamics in word embeddings on a large scale in a purely data-driven manner. In experiments on two different newspaper corpora, t...

Dari Dataset for Named Entity Recognition DariNER2

Zia, Ghezal Ahmad Jan (2020-08-08)

DariNER2 is the release of the Dari sentence-level Named Entity annotated dataset, collected from Dari Azadi Radio. The goal of the project was to annotate a corpus comprising various genres of text (news, newsgroups, and interviews) in the Dari language with structural information (syntax). In addition, it is developed to support sentence-level ambiguity in the Dari text. It contains 883 sent...

Dari Language Stopword Lists

Zia, Ghezal Ahmad Jan (2020-08-03)

The following is a list of stop words that are collected from books and newspapers that all follow Dari pure orthographic structure. These are frequently used in the Dari language but do not carry meaningful information for some language modeling tasks. This list of words reduces the noise in textual data and is excluded from the analysis. We always welcome, if you have any idea to change or su...

Dari Dataset for Part-of-Speech

Zia, Ghezal Ahmad Jan (2020-07-25)

This dataset is related to the task of part-of-speech tagging on the Dari language. It will be usable for many tasks of Natural Language processing on Dari text. The size of the dataset is 12K and it is annotated manually. The tagset used in this dataset is the Universal Tagger.

Dari Dataset for Named Entity Recognition DariNER1

Zia, Ghezal Ahmad Jan (2020-07-23)

DariNER1 is the collection of the data from Dari newswire domains. This dataset is developed based on the IO encoding scheme which following four types of named entities such as Person, Location, Organization, and Miscellaneous. The data follow the Dari pure orthographic structure and collected from Dari VOA news, Azadi Radio and Kankor (University National Entry Exam) from Higher Education of...

Information flow analysis of discrete embedded control system models

Mikulcak, Marcus (2020)

Embedded systems used in safety-critical domains have to uphold strict safety and security requirements. At the same time, their complexity has been strongly increasing across application domains. To manage this rise in complexity, manufacturers have shifted towards model-driven development methodologies. While successful in managing the complexity in the development of large, interconnected sy...

Elements of efficient data reduction: fractals, diminishers, weights and neighborhoods

Fluschnik, Till (2020)

Preprocessing and data reduction are basic algorithmic tools. In parameterized algorithmics, such preprocessing is defined by (problem) kernelization, where an equivalent instance (the kernel) is computed in polynomial time and its size can be upper-bounded only in a function of the parameter value of the input instance. In this thesis, we study lower and upper bounds on kernelization regarding...

Quantum-Chemical Insights from Interpretable Atomistic Neural Networks

Schütt, Kristof T. ; Gastegger, Michael ; Tkatchenko, Alexandre ; Müller, Klaus-Robert (2019-09-10)

With the rise of deep neural networks for quantum chemistry applications, there is a pressing need for architectures that, beyond delivering accurate predictions of chemical properties, are readily interpretable by researchers. Here, we describe interpretation techniques for atomistic neural networks on the example of Behler–Parrinello networks as well as the end-to-end model SchNet. Both model...

Molecular Dynamics with Neural Network Potentials

Gastegger, Michael ; Marquetand, Philipp (2020-06-04)

Molecular dynamics simulations are an important tool for describing the evolution of a chemical system with time. However, these simulations are inherently held back either by the prohibitive cost of accurate electronic structure theory computations or the limited accuracy of classical empirical force fields. Machine learning techniques can help to overcome these limitations by providing access...

Hamiltonian datasets: "Unifying machine learning and quantum chemistry with a deep neural network for molecular wavefunctions""

Schütt, Kristof T. ; Gastegger, Michael ; Tkatchenko, Alexandre ; Müller, Klaus-Robert ; Maurer, Reinhard J. (2019-09-25)

Machine learning advances chemistry and materials science by enabling large-scale exploration of chemical space based on quantum chemical calculations. While these models supply fast and accurate predictions of atomistic chemical properties, they do not explicitly capture the electronic degrees of freedom of a molecule, which limits their applicability for reactive chemistry and chemical analys...

Similarity encoder: A neural network architecture for learning similarity preserving embeddings

Horn, Franziska (2020)

Matrix factorization is at the heart of many machine learning algorithms, for example, for dimensionality reduction (e.g. kernel PCA) or recommender systems relying on collaborative filtering. Understanding a singular value decomposition (SVD) of a matrix as a neural network optimization problem enables us to decompose large matrices efficiently while dealing naturally with missing values in th...

Corona Twitter Dataset: 16 February 2020 - 03 March 2020

Brandl, Stephanie ; Lassner, David (2020)

For this dataset we have downloaded 22376075 tweets in 64 languages from 16 February until 03 March 2020. We used the following keywords: CORONA, CORONAVIRUS and #COVID-19.

BCI under distraction: Motor imagery in a pseudo realistic environment

Brandl, Stephanie (2015)

With the aim to investigate BCI in a pseudo-realistic environment, we recorded EEG from 16 healthy participants. Participants were asked to perform motor imagery tasks while dealing with different types of distractions such as vibratory stimulations or listening tasks.