Technische Universität Darmstadt

About: Technische Universität Darmstadt is a education organization based out in Darmstadt, Germany. It is known for research contribution in the topics: Computer science & Context (language use). The organization has 17316 authors who have published 40619 publications receiving 937916 citations. The organization is also known as: Darmstadt University of Technology & University of Darmstadt.

UKP: Computing Semantic Textual Similarity by Combining Multiple Content Similarity Measures

Abstract: We present the UKP system which performed best in the Semantic Textual Similarity (STS) task at SemEval-2012 in two out of three metrics. It uses a simple log-linear regression model, trained on the training data, to combine multiple text similarity measures of varying complexity. These range from simple character and word n-grams and common subsequences to complex features such as Explicit Semantic Analysis vector comparisons and aggregation of word similarity based on lexical-semantic resources. Further, we employ a lexical substitution system and statistical machine translation to add additional lexemes, which alleviates lexical gaps. Our final models, one per dataset, consist of a log-linear combination of about 20 features, out of the possible 300+ features implemented.

DÏoT: A Federated Self-learning Anomaly Detection System for IoT

Abstract: IoT devices are increasingly deployed in daily life. Many of these devices are, however, vulnerable due to insecure design, implementation, and configuration. As a result, many networks already have vulnerable IoT devices that are easy to compromise. This has led to a new category of malware specifically targeting IoT devices. However, existing intrusion detection techniques are not effective in detecting compromised IoT devices given the massive scale of the problem in terms of the number of different types of devices and manufacturers involved. In this paper, we present DIoT, an autonomous self-learning distributed system for detecting compromised IoT devices. DIoT builds effectively on device-type-specific communication profiles without human intervention nor labeled data that are subsequently used to detect anomalous deviations in devices' communication behavior, potentially caused by malicious adversaries. DIoT utilizes a federated learning approach for aggregating behavior profiles efficiently. To the best of our knowledge, it is the first system to employ a federated learning approach to anomaly-detection-based intrusion detection. Consequently, DIoT can cope with emerging new and unknown attacks. We systematically and extensively evaluated more than 30 off-the-shelf IoT devices over a long term and show that DIoT is highly effective (95.6% detection rate) and fast (257 ms) at detecting devices compromised by, for instance, the infamous Mirai malware. DIoT reported no false alarms when evaluated in a real-world smart home deployment setting.

Novel Nanocomposite Pt/RuO2⋅x H2O/Carbon Nanotube Catalysts for Direct Methanol Fuel Cells

Abstract: The perfect combination: RuO2⋅x H2O for donating and accepting protons and electrons and carbon nanotubes (CNTs) for compensating the loss of electron conductivity caused by the RuO2 coating, improving the electrode microstructure, and lowering the electrode resistance. The result: superb performance of the title catalyst for direct electrooxidation of methanol.

Radioactivity and thermalization in the ejecta of compact object mergers and their impact on kilonova light curves

Abstract: One promising electromagnetic signature of compact object mergers are kilonovae: approximately isotropic radioactively powered transients that peak days to weeks post-merger. Key uncertainties in kilonova modeling include the emission profiles of the radioactive decay products—non-thermal $\beta $-particles, $\alpha $-particles, fission fragments, and $\gamma $-rays—and the efficiency with which their kinetic energy is absorbed by the ejecta. The radioactive energy emitted, along with its thermalization efficiency, sets the luminosity budget and is therefore crucial for predicting kilonova light curves. We outline uncertainties in the radioactivity, describe the processes by which the decay products transfer energy to the ejecta, and calculate time-dependent thermalization efficiencies for each particle type. We determine the net thermalization efficiency and explore its dependence on r-process yields—in particular, the production of $\alpha $-decaying translead nuclei—and on ejecta mass, velocity, and magnetic fields. We incorporate our results into detailed radiation transport simulations, and calculate updated kilonova light curve predictions. Thermalization effects reduce kilonova luminosities by a factor of roughly 2 at peak, and by an order of magnitude at later times (15 days or more after explosion). We present analytic fits to time-dependent thermalization efficiencies, which can be used to improve light curve models. We revisit the putative kilonova that accompanied gamma-ray burst 130603B, and estimate the mass ejected in that event. We find later time kilonova light curves can be significantly impacted by $\alpha $-decay from translead isotopes, data at these times may therefore be diagnostic of ejecta abundances.