Showing papers by "Bauhaus University, Weimar published in 2017"

A Stylometric Inquiry into Hyperpartisan and Fake News

Abstract: This paper reports on a writing style analysis of hyperpartisan (i.e., extremely one-sided) news in connection to fake news. It presents a large corpus of 1,627 articles that were manually fact-checked by professional journalists from BuzzFeed. The articles originated from 9 well-known political publishers, 3 each from the mainstream, the hyperpartisan left-wing, and the hyperpartisan right-wing. In sum, the corpus contains 299 fake news, 97% of which originated from hyperpartisan publishers. We propose and demonstrate a new way of assessing style similarity between text categories via Unmasking---a meta-learning approach originally devised for authorship verification---, revealing that the style of left-wing and right-wing news have a lot more in common than any of the two have with the mainstream. Furthermore, we show that hyperpartisan news can be discriminated well by its style from the mainstream (F1=0.78), as can be satire from both (F1=0.81). Unsurprisingly, style-based fake news detection does not live up to scratch (F1=0.46). Nevertheless, the former results are important to implement pre-screening for fake news detectors.

Stochastic analysis of the fracture toughness of polymeric nanoparticle composites using polynomial chaos expansions

Abstract: The fracture energy is a substantial material property that measures the ability of materials to resist crack growth. The reinforcement of the epoxy polymers by nanosize fillers improves significantly their toughness. The fracture mechanism of the produced polymeric nanocomposites is influenced by different parameters. This paper presents a methodology for stochastic modelling of the fracture in polymer/particle nanocomposites. For this purpose, we generated a 2D finite element model containing an epoxy matrix and rigid nanoparticles surrounded by an interphase zone. The crack propagation was modelled by the phantom node method. The stochastic model is based on six uncertain parameters: the volume fraction and the diameter of the nanoparticles, Young’s modulus and the maximum allowable principal stress of the epoxy matrix, the interphase zone thickness and its Young’s modulus. Considering the uncertainties in input parameters, a polynomial chaos expansion surrogate model is constructed followed by a sensitivity analysis. The variance in the fracture energy was mostly influenced by the maximum allowable principal stress and Young’s modulus of the epoxy matrix.

CoNLL 2017 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies

Abstract: The Conference on Computational Natural Language Learning (CoNLL) features a shared task, in which participants train and test their learning systems on the same data sets. In 2017, the task was devoted to learning dependency parsers for a large number of languages, in a real-world setting without any gold-standard annotation on input. All test sets followed a unified annotation scheme, namely that of Universal Dependencies. In this paper, we define the task and evaluation methodology, describe how the data sets were prepared, report and analyze the main results, and provide a brief categorization of the different approaches of the participating systems.

Computational Argumentation Quality Assessment in Natural Language

Abstract: Research on computational argumentation faces the problem of how to automatically assess the quality of an argument or argumentation. While different quality dimensions have been approached in natural language processing, a common understanding of argumentation quality is still missing. This paper presents the first holistic work on computational argumentation quality in natural language. We comprehensively survey the diverse existing theories and approaches to assess logical, rhetorical, and dialectical quality dimensions, and we derive a systematic taxonomy from these. In addition, we provide a corpus with 320 arguments, annotated for all 15 dimensions in the taxonomy. Our results establish a common ground for research on computational argumentation quality assessment.

First-principles investigation of mechanical properties of silicene, germanene and stanene

Abstract: Two-dimensional allotropes of group-IV substrates including silicene, germanene and stanene have recently attracted considerable attention in nanodevice fabrication industry. These materials involving the buckled structure have been experimentally fabricated lately. In this study, first-principles density functional theory calculations were utilized to investigate the mechanical properties of single-layer and free-standing silicene, germanene and stanene. Uniaxial tensile and compressive simulations were carried out to probe and compare stress-strain properties; such as the Young’s modulus, Poisson’s ratio and ultimate strength. We evaluated the chirality effect on the mechanical response and bond structure of the 2D substrates. Our first-principles simulations suggest that in all studied samples application of uniaxial loading can alter the electronic nature of the buckled structures into the metallic character. Our investigation provides a general but also useful viewpoint with respect to the mechanical properties of silicene, germanene and stanene.

Building an Argument Search Engine for the Web

Abstract: Computational argumentation is expected to play a critical role in the future of web search. To make this happen, many search-related questions must be revisited, such as how people query for arguments, how to mine arguments from the web, or how to rank them. In this paper, we develop an argument search framework for studying these and further questions. The framework allows for the composition of approaches to acquiring, mining, assessing, indexing, querying, retrieving, ranking, and presenting arguments while relying on standard infrastructure and interfaces. Based on the framework, we build a prototype search engine, called args, that relies on an initial, freely accessible index of nearly 300k arguments crawled from reliable web resources. The framework and the argument search engine are intended as an environment for collaborative research on computational argumentation and its practical evaluation.

Adsorption of Metallic, Metalloidic, and Nonmetallic Adatoms on Two-Dimensional C3N

Abstract: Two-dimensional polyaniline with a C3N stoichiometry is a newly fabricated material that is expected to possess fascinating electronic, thermal, mechanical, and chemical properties. The possibility of further tuning the C3N properties upon the adsorption of foreign adatoms is thus among the most attractive research. We carried out extensive ab initio density functional theory simulations to investigate the adsorption of various elements including nonmetallic, metalloidic, and metallic elements on the C3N monolayer. While pristine C3N acts as a semiconductor with an indirect electronic band gap; the functionalization with nonmetallic and semimetallic elements leads to a p-type doping and induces metallic behavior to the monolayer. On the contrary, metallic adsorption depending on the adatom size and the number of valence electrons may result in semiconducting, half-metallic, or metallic properties. Whenever metallic foreign atoms conduct metallic characteristics, they mostly lead to the n-type doping by el...

Flat borophene films as anode materials for Mg, Na or Li-ion batteries with ultra high capacities: A first-principles study

Abstract: Most recent exciting experimental advances introduced buckled and flat borophene nanomembranes as new members to the advancing family of two-dimensional (2D) materials. Borophene, is the boron atom analogue of graphene with interesting properties suitable for a wide variety of applications. In this investigation, we conducted extensive first-principles density functional theory simulations to explore the application of four different flat borophene films as anode materials for Al, Mg, Na or Li-ion batteries. In our modelling, first the strongest binding sites were predicted and next we gradually increased the adatoms coverage until the maximum capacity was reached. Bader charge analysis was employed to evaluate the charge transfer between the adatoms and the borophene films. Nudged elastic band method was also utilized to probe the ions diffusions. We calculated the average atom adsorption energies and open-circuit voltage profiles as a function of adatoms coverage. Our findings propose the flat borophene films as electrically conductive and thermally stable anode materials with ultra high capacities of 2480 mAh/g, 1640 mAh/g and 2040 mAh/g for Mg, Na or Li-ion batteries, respectively, which distinctly outperform not only the buckled borophene but also all other 2D materials. Our study may provide useful viewpoint with respect to the possible application of flat borophene films for the design of high capacity and light weight advanced rechargeable ion batteries.

Finding near-optimal configurations in product lines by random sampling

Abstract: Software Product Lines (SPLs) are highly configurable systems. This raises the challenge to find optimal performing configurations for an anticipated workload. As SPL configuration spaces are huge, it is infeasible to benchmark all configurations to find an optimal one. Prior work focused on building performance models to predict and optimize SPL configurations. Instead, we randomly sample and recursively search a configuration space directly to find near-optimal configurations without constructing a prediction model. Our algorithms are simpler and have higher accuracy and efficiency.

Using bad learners to find good configurations

Abstract: Finding the optimally performing configuration of a software system for a given setting is often challenging. Recent approaches address this challenge by learning performance models based on a sample set of configurations. However, building an accurate performance model can be very expensive (and is often infeasible in practice). The central insight of this paper is that exact performance values (e.g., the response time of a software system) are not required to rank configurations and to identify the optimal one. As shown by our experiments, performance models that are cheap to learn but inaccurate (with respect to the difference between actual and predicted performance) can still be used rank configurations and hence find the optimal configuration. This novel rank-based approach allows us to significantly reduce the cost (in terms of number of measurements of sample configuration) as well as the time required to build performance models. We evaluate our approach with 21 scenarios based on 9 software systems and demonstrate that our approach is beneficial in 16 scenarios; for the remaining 5 scenarios, an accurate model can be built by using very few samples anyway, without the need for a rank-based approach.

TL;DR: Mining Reddit to Learn Automatic Summarization

Abstract: Recent advances in automatic text summarization have used deep neural networks to generate high-quality abstractive summaries, but the performance of these models strongly depends on large amounts of suitable training data. We propose a new method for mining social media for author-provided summaries, taking advantage of the common practice of appending a “TL;DR” to long posts. A case study using a large Reddit crawl yields the Webis-TLDR-17 dataset, complementing existing corpora primarily from the news genre. Our technique is likely applicable to other social media sites and general web crawls.

Topology optimization of flexoelectric structures

Abstract: We present a mixed finite element formulation for flexoelectric nanostructures that is coupled with topology optimization to maximize their intrinsic material performance with regards to their energy conversion potential. Using Barium Titanate (BTO) as the model flexoelectric material, we demonstrate the significant enhancement in energy conversion that can be obtained using topology optimization. We also demonstrate that non-smooth surfaces can play a key role in the energy conversion enhancements obtained through topology optimization. Finally, we examine the relative benefits of flexoelectricity, and surface piezoelectricity on the energy conversion efficiency of nanobeams. We find that the energy conversion efficiency of flexoelectric nanobeams is comparable to the energy conversion efficiency obtained from nanobeams whose electromechanical coupling occurs through surface piezoelectricity, but are ten times thinner. Overall, our results not only demonstrate the utility and efficiency of flexoelectricity as a nanoscale energy conversion mechanism, but also its relative superiority as compared to piezoelectric or surface piezoelectric effects.

Transfer learning for improving model predictions in highly configurable software

Abstract: Modern software systems are built to be used in dynamic environments using configuration capabilities to adapt to changes and external uncertainties. In a self-adaptation context, we are often interested in reasoning about the performance of the systems under different configurations. Usually, we learn a black-box model based on real measurements to predict the performance of the system given a specific configuration. However, as modern systems become more complex, there are many configuration parameters that may interact and we end up learning an exponentially large configuration space. Naturally, this does not scale when relying on real measurements in the actual changing environment. We propose a different solution: Instead of taking the measurements from the real system, we learn the model using samples from other sources, such as simulators that approximate performance of the real system at low cost. We define a cost model that transform the traditional view of model learning into a multi-objective problem that not only takes into account model accuracy but also measurements effort as well. We evaluate our cost-aware transfer learning solution using real-world configurable software including (i) a robotic system, (ii) 3 different stream processing applications, and (iii) a NoSQL database system. The experimental results demonstrate that our approach can achieve (a) a high prediction accuracy, as well as (b) a high model reliability.

Mechanical responses of borophene sheets: A first-principles study

Abstract: Recent experimental advances for the fabrication of various borophene sheets introduced new structures with a wide prospect of applications. Borophene is the boron atoms analogue of graphene. Borophene exhibits various structural polymorphs all of which are metallic. In this work, we employed first-principles density functional theory calculations to investigate the mechanical properties of five different single-layer borophene sheets. In particular, we analyzed the effect of loading direction and point vacancy on the mechanical response of borophene. Moreover, we compared the thermal stabilities of the considered borophene systems. Based on the results of our modelling, borophene films depending on the atomic configurations and the loading direction can yield remarkable elastic modulus in the range of 163-382 GPa.nm and high ultimate tensile strength from 13.5 GPa.nm to around 22.8 GPa.nm at the corresponding strain from 0.1 to 0.21. Our study reveals the remarkable mechanical characteristics of borophene films.

Transfer learning for performance modeling of configurable systems: an exploratory analysis

Abstract: Modern software systems provide many configuration options which significantly influence their non-functional properties. To understand and predict the effect of configuration options, several sampling and learning strategies have been proposed, albeit often with significant cost to cover the highly dimensional configuration space. Recently, transfer learning has been applied to reduce the effort of constructing performance models by transferring knowledge about performance behavior across environments. While this line of research is promising to learn more accurate models at a lower cost, it is unclear why and when transfer learning works for performance modeling. To shed light on when it is beneficial to apply transfer learning, we conducted an empirical study on four popular software systems, varying software configurations and environmental conditions, such as hardware, workload, and software versions, to identify the key knowledge pieces that can be exploited for transfer learning. Our results show that in small environmental changes (e.g., homogeneous workload change), by applying a linear transformation to the performance model, we can understand the performance behavior of the target environment, while for severe environmental changes (e.g., drastic workload change) we can transfer only knowledge that makes sampling more efficient, e.g., by reducing the dimensionality of the configuration space.

Gimli : A Cross-Platform Permutation

Abstract: This paper presents Gimli, a 384-bit permutation designed to achieve high security with high performance across a broad range of platforms, including 64-bit Intel/AMD server CPUs, 64-bit and 32-bit ARM smartphone CPUs, 32-bit ARM microcontrollers, 8-bit AVR microcontrollers, FPGAs, ASICs without side-channel protection, and ASICs with side-channel protection.

Technology and applications for collaborative learning in virtual reality

Abstract: In this symposium we explore the immense potential for virtual reality to be applied in educational settings. We discuss recent technological developments against a backdrop of several decades of research. Six presentations, including four from academic authors and two from the commercial sector, will explore user requirements, new technologies, and practical issues in collaborative VR applications for learning

Electrical and thermal transport in coplanar polycrystalline graphene-hBN heterostructures

Abstract: We present a theoretical study of electronic and thermal transport in polycrystalline heterostructures combining graphene (G) and hexagonal boron nitride (hBN) grains of varying size and distribution. By increasing the hBN grain density from a few percent to 100%, the system evolves from a good conductor to an insulator, with the mobility dropping by orders of magnitude and the sheet resistance reaching the MΩ regime. The Seebeck coefficient is suppressed above 40% mixing, while the thermal conductivity of polycrystalline hBN is found to be on the order of 30–120 Wm–1 K–1. These results, agreeing with available experimental data, provide guidelines for tuning G-hBN properties in the context of two-dimensional materials engineering. In particular, while we proved that both electrical and thermal properties are largely affected by morphological features (e.g., by the grain size and composition), we find in all cases that nanometer-sized polycrystalline G-hBN heterostructures are not good thermoelectric mate...