Refine
H-BRS Bibliography
- yes (50) (remove)
Departments, institutes and facilities
- Fachbereich Informatik (50) (remove)
Document Type
- Preprint (50) (remove)
Year of publication
Language
- English (50)
Keywords
- Evolutionary Computation (2)
- FOS: Computer and information sciences (2)
- AML (1)
- Air Pollution Monitoring (1)
- Artificial Intelligence (cs.AI) (1)
- Authentication features (1)
- Autoencoder (1)
- Automatic Short Answer Grading (1)
- BERT (1)
- Ball Tracking (1)
- Bayesian Deep Learning (1)
- Bayesian optimization (1)
- Big Data Analysis (1)
- Bioinformatics (1)
- Black-Box Optimization (1)
- Compositional Pattern Producing Networks (1)
- Computational Fluid Dynamics (1)
- Computer Science - Computer Vision and Pattern Recognition (1)
- Computer Science - Learning (1)
- Cutting sticks problem (1)
- Deep Learning (1)
- Drosophila (1)
- Drug (1)
- ELMo (1)
- Facial Emotion Recognition (1)
- Feature Model (1)
- Filtering (1)
- GPT (1)
- GPT-2 (1)
- HSP90 (1)
- Heat Shock Protein (1)
- Hyper-parameter Tuning (1)
- IoT (1)
- Knowledge Graphs (1)
- LOTUS Sensor Node (1)
- Lattice Boltzmann Method (1)
- Lennard-Jones parameters (1)
- Leukemia (1)
- Level-of-Detail (1)
- LoRa (1)
- LoRaWAN (1)
- Low-Power Wide Area Network (LP-WAN) (1)
- MESD (1)
- Machine Learning (1)
- Machine Learning (cs.LG) (1)
- Measurement (1)
- Mebendazole (1)
- Molecular dynamics (1)
- Multi-Solution Optimization (1)
- Natural Language Processing (1)
- Navigation (1)
- Optical Flow (1)
- Path Loss (1)
- Quality Diversity (1)
- Quality diversity (1)
- Quantum mechanics (1)
- Real-Time Image Processing (1)
- Rendering (1)
- Risk-based Authentication (RBA) (1)
- Robot Perception (1)
- Robotics (1)
- Robotics (cs.RO) (1)
- SEMA (1)
- Scale Tuning (1)
- Set partition problem (1)
- Side Channel Analysis (1)
- Spherical Treadmill (1)
- Tautomers (1)
- TinyECC 2.0 (1)
- Transfer Learning (1)
- Transformers (1)
- Uncertainty Quantification (1)
- Urban (1)
- Usable Security (1)
- Virtual Reality (1)
- Wireless Sensor Network (1)
- caching (1)
- cellular automata (1)
- computer vision (1)
- convolutional neural networks (1)
- designing air flow (1)
- diversity (1)
- domain adaptation (1)
- feature discovery (1)
- force field (1)
- genetic neutrality (1)
- global illumination (1)
- local optimization (1)
- low-cost air sensor (1)
- multi-objective optimization (1)
- multimodal optimization (1)
- multiscale parameterization (1)
- non-linear projection (1)
- object detection (1)
- objective function (1)
- parametric (1)
- phenotypic diversity (1)
- phenotypic feature (1)
- phenotypic niching (1)
- rds encoding (1)
- representation (1)
- surrogate assisted phenotypic niching (1)
- surrogate models (1)
- traffic surveillance (1)
- transfer learning (1)
- unsupervised learning (1)
- weighting factors (1)
- wind nuisance threshold (1)
Risk-based authentication (RBA) aims to strengthen password-based authentication rather than replacing it. RBA does this by monitoring and recording additional features during the login process. If feature values at login time differ significantly from those observed before, RBA requests an additional proof of identification. Although RBA is recommended in the NIST digital identity guidelines, it has so far been used almost exclusively by major online services. This is partly due to a lack of open knowledge and implementations that would allow any service provider to roll out RBA protection to its users.
To close this gap, we provide a first in-depth analysis of RBA characteristics in a practical deployment. We observed N=780 users with 247 unique features on a real-world online service for over 1.8 years. Based on our collected data set, we provide (i) a behavior analysis of two RBA implementations that were apparently used by major online services in the wild, (ii) a benchmark of the features to extract a subset that is most suitable for RBA use, (iii) a new feature that has not been used in RBA before, and (iv) factors which have a significant effect on RBA performance. Our results show that RBA needs to be carefully tailored to each online service, as even small configuration adjustments can greatly impact RBA's security and usability properties. We provide insights on the selection of features, their weightings, and the risk classification in order to benefit from RBA after a minimum number of login attempts.
Background: Virtual reality combined with spherical treadmills is used across species for studying neural circuits underlying navigation.
New Method: We developed an optical flow-based method for tracking treadmil ball motion in real-time using a single high-resolution camera.
Results: Tracking accuracy and timing were determined using calibration data. Ball tracking was performed at 500 Hz and integrated with an open source game engine for virtual reality projection. The projection was updated at 120 Hz with a latency with respect to ball motion of 30 ± 8 ms.
Comparison: with Existing Method(s) Optical flow based tracking of treadmill motion is typically achieved using optical mice. The camera-based optical flow tracking system developed here is based on off-the-shelf components and offers control over the image acquisition and processing parameters. This results in flexibility with respect to tracking conditions – such as ball surface texture, lighting conditions, or ball size – as well as camera alignment and calibration.
Conclusions: A fast system for rotational ball motion tracking suitable for virtual reality animal behavior across different scales was developed and characterized.
TSEM: Temporally Weighted Spatiotemporal Explainable Neural Network for Multivariate Time Series
(2022)
Deep learning has become a one-size-fits-all solution for technical and business domains thanks to its flexibility and adaptability. It is implemented using opaque models, which unfortunately undermines the outcome trustworthiness. In order to have a better understanding of the behavior of a system, particularly one driven by time series, a look inside a deep learning model so-called posthoc eXplainable Artificial Intelligence (XAI) approaches, is important. There are two major types of XAI for time series data, namely model-agnostic and model-specific. Model-specific approach is considered in this work. While other approaches employ either Class Activation Mapping (CAM) or Attention Mechanism, we merge the two strategies into a single system, simply called the Temporally Weighted Spatiotemporal Explainable Neural Network for Multivariate Time Series (TSEM). TSEM combines the capabilities of RNN and CNN models in such a way that RNN hidden units are employed as attention weights for the CNN feature maps temporal axis. The result shows that TSEM outperforms XCM. It is similar to STAM in terms of accuracy, while also satisfying a number of interpretability criteria, including causality, fidelity, and spatiotemporality.
In an effort to assist researchers in choosing basis sets for quantum mechanical modeling of molecules (i.e. balancing calculation cost versus desired accuracy), we present a systematic study on the accuracy of computed conformational relative energies and their geometries in comparison to MP2/CBS and MP2/AV5Z data, respectively. In order to do so, we introduce a new nomenclature to unambiguously indicate how a CBS extrapolation was computed. Nineteen minima and transition states of buta-1,3-diene, propan-2-ol and the water dimer were optimized using forty-five different basis sets. Specifically, this includes one Pople (i.e. 6-31G(d)), eight Dunning (i.e. VXZ and AVXZ, X=2-5), twenty-five Jensen (i.e. pc-n, pcseg-n, aug-pcseg-n, pcSseg-n and aug-pcSseg-n, n=0-4) and nine Karlsruhe (e.g. def2-SV(P), def2-QZVPPD) basis sets. The molecules were chosen to represent both common and electronically diverse molecular systems. In comparison to MP2/CBS relative energies computed using the largest Jensen basis sets (i.e. n=2,3,4), the use of smaller sizes (n=0,1,2 and n=1,2,3) provides results that are within 0.11--0.24 and 0.09-0.16 kcal/mol. To practically guide researchers in their basis set choice, an equation is introduced that ranks basis sets based on a user-defined balance between their accuracy and calculation cost. Furthermore, we explain why the aug-pcseg-2, def2-TZVPPD and def2-TZVP basis sets are very suitable choices to balance speed and accuracy.
The prototype of a workflow system for the submission of content to a digital object repository is here presented. It is based entirely on open-source standard components and features a service-oriented architecture. The front-end consists of Java Business Process Management (jBPM), Java Server Faces (JSF), and Java Server Pages (JSP). A Fedora Repository and a mySQL data base management system serve as a back-end. The communication between front-end and back-end uses a SOAP minimal binding stub. We describe the design principles and the construction of the prototype and discuss the possibilities and limitations of work ow creation by administrators. The code of the prototype is open-source and can be retrieved in the project escipub at http://sourceforge.net/ .
The majority of biomedical knowledge is stored in structured databases or as unstructured text in scientific publications. This vast amount of information has led to numerous machine learning-based biological applications using either text through natural language processing (NLP) or structured data through knowledge graph embedding models (KGEMs). However, representations based on a single modality are inherently limited. To generate better representations of biological knowledge, we propose STonKGs, a Sophisticated Transformer trained on biomedical text and Knowledge Graphs. This multimodal Transformer uses combined input sequences of structured information from KGs and unstructured text data from biomedical literature to learn joint representations. First, we pre-trained STonKGs on a knowledge base assembled by the Integrated Network and Dynamical Reasoning Assembler (INDRA) consisting of millions of text-triple pairs extracted from biomedical literature by multiple NLP systems. Then, we benchmarked STonKGs against two baseline models trained on either one of the modalities (i.e., text or KG) across eight different classification tasks, each corresponding to a different biological application. Our results demonstrate that STonKGs outperforms both baselines, especially on the more challenging tasks with respect to the number of classes, improving upon the F1-score of the best baseline by up to 0.083. Additionally, our pre-trained model as well as the model architecture can be adapted to various other transfer learning applications. Finally, the source code and pre-trained STonKGs models are available at https://github.com/stonkgs/stonkgs and https://huggingface.co/stonkgs/stonkgs-150k.
TinyECC 2.0 is an open source library for Elliptic Curve Cryptography (ECC) in wireless sensor networks. This paper analyzes the side channel susceptibility of TinyECC 2.0 on a LOTUS sensor node platform. In our work we measured the electromagnetic (EM) emanation during computation of the scalar multiplication using 56 different configurations of TinyECC 2.0. All of them were found to be vulnerable, but to a different degree. The different degrees of leakage include adversary success using (i) Simple EM Analysis (SEMA) with a single measurement, (ii) SEMA using averaging, and (iii) Multiple-Exponent Single-Data (MESD) with a single measurement of the secret scalar. It is extremely critical that in 30 TinyECC 2.0 configurations a single EM measurement of an ECC private key operation is sufficient to simply read out the secret scalar. MESD requires additional adversary capabilities and it affects all TinyECC 2.0 configurations, again with only a single measurement of the ECC private key operation. These findings give evidence that in security applications a configuration of TinyECC 2.0 should be chosen that withstands SEMA with a single measurement and, beyond that, an addition of appropriate randomizing countermeasures is necessary.
Self-supervised learning has proved to be a powerful approach to learn image representations without the need of large labeled datasets. For underwater robotics, it is of great interest to design computer vision algorithms to improve perception capabilities such as sonar image classification. Due to the confidential nature of sonar imaging and the difficulty to interpret sonar images, it is challenging to create public large labeled sonar datasets to train supervised learning algorithms. In this work, we investigate the potential of three self-supervised learning methods (RotNet, Denoising Autoencoders, and Jigsaw) to learn high-quality sonar image representation without the need of human labels. We present pre-training and transfer learning results on real-life sonar image datasets. Our results indicate that self-supervised pre-training yields classification performance comparable to supervised pre-training in a few-shot transfer learning setup across all three methods. Code and self-supervised pre-trained models are be available at https://github.com/agrija9/ssl-sonar-images
Vietnam requires a sustainable urbanization, for which city sensing is used in planning and de-cision-making. Large cities need portable, scalable, and inexpensive digital technology for this purpose. End-to-end air quality monitoring companies such as AirVisual and Plume Air have shown their reliability with portable devices outfitted with superior air sensors. They are pricey, yet homeowners use them to get local air data without evaluating the causal effect. Our air quality inspection system is scalable, reasonably priced, and flexible. Minicomputer of the sys-tem remotely monitors PMS7003 and BME280 sensor data through a microcontroller processor. The 5-megapixel camera module enables researchers to infer the causal relationship between traffic intensity and dust concentration. The design enables inexpensive, commercial-grade hardware, with Azure Blob storing air pollution data and surrounding-area imagery and pre-venting the system from physically expanding. In addition, by including an air channel that re-plenishes and distributes temperature, the design improves ventilation and safeguards electrical components. The gadget allows for the analysis of the correlation between traffic and air quali-ty data, which might aid in the establishment of sustainable urban development plans and poli-cies.
Saliency methods are frequently used to explain Deep Neural Network-based models. Adebayo et al.'s work on evaluating saliency methods for classification models illustrate certain explanation methods fail the model and data randomization tests. However, on extending the tests for various state of the art object detectors we illustrate that the ability to explain a model is more dependent on the model itself than the explanation method. We perform sanity checks for object detection and define new qualitative criteria to evaluate the saliency explanations, both for object classification and bounding box decisions, using Guided Backpropagation, Integrated Gradients, and their Smoothgrad versions, together with Faster R-CNN, SSD, and EfficientDet-D0, trained on COCO. In addition, the sensitivity of the explanation method to model parameters and data labels varies class-wise motivating to perform the sanity checks for each class. We find that EfficientDet-D0 is the most interpretable method independent of the saliency method, which passes the sanity checks with little problems.