Showing papers by "Bauhaus University, Weimar published in 2018"

A Stylometric Inquiry into Hyperpartisan and Fake News

Abstract: We report on a comparative style analysis of hyperpartisan (extremely one-sided) news and fake news. A corpus of 1,627 articles from 9 political publishers, three each from the mainstream, the hyperpartisan left, and the hyperpartisan right, have been fact-checked by professional journalists at BuzzFeed: 97% of the 299 fake news articles identified are also hyperpartisan. We show how a style analysis can distinguish hyperpartisan news from the mainstream (F1 = 0.78), and satire from both (F1 = 0.81). But stylometry is no silver bullet as style-based fake news detection does not work (F1 = 0.46). We further reveal that left-wing and right-wing news share significantly more stylistic similarities than either does with the mainstream. This result is robust: it has been confirmed by three different modeling approaches, one of which employs Unmasking in a novel way. Applications of our results include partisanship detection and pre-screening for semi-automatic fake news detection.

The Argument Reasoning Comprehension Task: Identification and Reconstruction of Implicit Warrants

Abstract: Reasoning is a crucial part of natural language argumentation. To comprehend an argument, one must analyze its warrant, which explains why its claim follows from its premises. As arguments are highly contextualized, warrants are usually presupposed and left implicit. Thus, the comprehension does not only require language understanding and logic skills, but also depends on common sense. In this paper we develop a methodology for reconstructing warrants systematically. We operationalize it in a scalable crowdsourcing process, resulting in a freely licensed dataset with warrants for 2k authentic arguments from news comments. On this basis, we present a new challenging task, the argument reasoning comprehension task. Given an argument with a claim and a premise, the goal is to choose the correct implicit warrant from two options. Both warrants are plausible and lexically close, but lead to contradicting claims. A solution to this task will define a substantial step towards automatic warrant reconstruction. However, experiments with several neural attention and language models reveal that current approaches do not suffice.

Application of ANNs, ANFIS and RSM to estimating and optimizing the parameters that affect the yield and cost of biodiesel production

Abstract: Biodiesel can easily be used as an alternative fuel in diesel engines. It is environmentally friendly and can be produced from low-cost feedstocks such as waste cooking oil (WCO). WCO contains a si...

Sugarcane growth prediction based on meteorological parameters using extreme learning machine and artificial neural network

Abstract: Management strategies for sustainable sugarcane production need to deal with the increasing complexity and variability of the whole sugar system. Moreover, they need to accommodate the multiple goals of different industry sectors and the wider community. Traditional disciplinary approaches are unable to provide integrated management solutions, and an approach based on whole systems analysis is essential to bring about beneficial change to industry and the community. The application of this approach to water management, environmental management and cane supply management is outlined, where the literature indicates that the application of extreme learning machine (ELM) has never been explored in this realm. Consequently, the leading objective of the current research was set to filling this gap by applying ELM to launch swift and accurate model for crop production data-driven. The key learning has been the need for innovation both in the technical aspects of system function underpinned by modelling of sugarcane growth. Therefore, the current study is an attempt to establish an integrate model using ELM to predict the concluding growth amount of sugarcane. Prediction results were evaluated and further compared with artificial neural network (ANN) and genetic programming models. Accuracy of the ELM model is calculated using the statistics indicators of Root Means Square Error (RMSE), Pearson Coefficient (r), and Coefficient of Determination (R2) with promising results of 0.8, 0.47, and 0.89, respectively. The results also show better generalization ability in addition to faster learning curve. Thus, proficiency of the ELM for supplementary work on advancement of prediction model for sugarcane growth was approved with promising results.

Boron–graphdiyne: a superstretchable semiconductor with low thermal conductivity and ultrahigh capacity for Li, Na and Ca ion storage

Abstract: Most recently, boron–graphdiyne, a π-conjugated two-dimensional (2D) structure made from a merely sp carbon skeleton connected with boron atoms was successfully experimentally realized through a bottom-up synthetic strategy. Motivated by this exciting experimental advance, we conducted density functional theory (DFT) and classical molecular dynamics simulations to study the mechanical, thermal conductivity and stability, electronic and optical properties of single-layer B-graphdiyne. We particularly analyzed the application of this novel 2D material as an anode for Li, Na, Mg and Ca ion storage. Uniaxial tensile simulation results reveal that B-graphdiyne owing to its porous structure and flexibility can yield superstretchability. The single-layer B-graphdiyne was found to exhibit a semiconducting electronic character, with a narrow band-gap of 1.15 eV based on the HSE06 prediction. It was confirmed that mechanical straining can be employed to further tune the optical absorbance and electronic band-gap of B-graphdiyne. Ab initio molecular dynamics results reveal that B-graphdiyne can withstand high temperatures, like 2500 K. The thermal conductivity of suspended single-layer B-graphdiyne was predicted to be very low, ∼2.5 W mK−1 at room temperature. Our first-principles results reveal the outstanding prospect of B-graphdiyne as an anode material with ultrahigh charge capacities of 808 mA h g−1, 5174 mA hg−1 and 3557 mA h g−1 for Na, Ca and Li ion storage, respectively. The comprehensive insight provided by this investigation highlights the outstanding physics of B-graphdiyne nanomembranes, and suggests them as highly promising candidates for the design of novel stretchable nanoelectronics and energy storage devices.

Borophene hydride: a stiff 2D material with high thermal conductivity and attractive optical and electronic properties.

Abstract: Two-dimensional (2D) structures of boron atoms, so-called borophene, have recently attracted remarkable attention. In a recent exciting experimental study, a hydrogenated borophene structure was realized. Motivated by this success, we conducted extensive first-principles calculations to explore the mechanical, thermal conduction, electronic and optical responses of borophene hydride. The mechanical response of borophene hydride was found to be anisotropic, with an elastic modulus of 131 N m−1 and a high tensile strength of 19.9 N m−1 along the armchair direction. Notably, it was shown that by applying mechanical loading the metallic electronic character of borophene hydride can be altered to direct band-gap semiconducting, very appealing for application in nanoelectronics. The absorption edge of the imaginary part of the dielectric function was found to occur in the visible range of light for parallel polarization. Finally, it was estimated that this novel 2D structure at room temperature can exhibit high thermal conductivities of 335 W mK−1 and 293 W mK−1 along the zigzag and armchair directions, respectively. Our study confirms that borophene hydride shows an outstanding combination of interesting mechanical, electronic, optical and thermal conduction properties, which are promising for the design of novel nanodevices.

The Revival of Private Landlords in Britain’s Post-Homeownership Society

Abstract: Homeownership has been declining in favour of private renting in most developed English speaking countries since the early-2000s. Public debates in countries like Britain, Australia and the US have...

Retrieval of the Best Counterargument without Prior Topic Knowledge

Abstract: Given any argument on any controversial topic, how to counter it? This question implies the challenging retrieval task of finding the best counterargument. Since prior knowledge of a topic cannot be expected in general, we hypothesize the best counterargument to invoke the same aspects as the argument while having the opposite stance. To operationalize our hypothesis, we simultaneously model the similarity and dissimilarity of pairs of arguments, based on the words and embeddings of the arguments’ premises and conclusions. A salient property of our model is its independence from the topic at hand, i.e., it applies to arbitrary arguments. We evaluate different model variations on millions of argument pairs derived from the web portal idebate.org. Systematic ranking experiments suggest that our hypothesis is true for many arguments: For 7.6 candidates with opposing stance on average, we rank the best counterargument highest with 60% accuracy. Even among all 2801 test set pairs as candidates, we still find the best one about every third time.

Data-efficient performance learning for configurable systems

Abstract: Many software systems today are configurable, offering customization of functionality by feature selection. Understanding how performance varies in terms of feature selection is key for selecting appropriate configurations that meet a set of given requirements. Due to a huge configuration space and the possibly high cost of performance measurement, it is usually not feasible to explore the entire configuration space of a configurable system exhaustively. It is thus a major challenge to accurately predict performance based on a small sample of measured system variants. To address this challenge, we propose a data-efficient learning approach, called DECART, that combines several techniques of machine learning and statistics for performance prediction of configurable systems. DECART builds, validates, and determines a prediction model based on an available sample of measured system variants. Empirical results on 10 real-world configurable systems demonstrate the effectiveness and practicality of DECART. In particular, DECART achieves a prediction accuracy of 90% or higher based on a small sample, whose size is linear in the number of features. In addition, we propose a sample quality metric and introduce a quantitative analysis of the quality of a sample for performance prediction.

An Intelligent Artificial Neural Network-Response Surface Methodology Method for Accessing the Optimum Biodiesel and Diesel Fuel Blending Conditions in a Diesel Engine from the Viewpoint of Exergy and Energy Analysis

Abstract: Biodiesel, as the main alternative fuel to diesel fuel which is produced from renewable and available resources, improves the engine emissions during combustion in diesel engines. In this study, the biodiesel is produced initially from waste cooking oil (WCO). The fuel samples are applied in a diesel engine and the engine performance has been considered from the viewpoint of exergy and energy approaches. Engine tests are performed at a constant 1500 rpm speed with various loads and fuel samples. The obtained experimental data are also applied to develop an artificial neural network (ANN) model. Response surface methodology (RSM) is employed to optimize the exergy and energy efficiencies. Based on the results of the energy analysis, optimal engine performance is obtained at 80% of full load in presence of B10 and B20 fuels. However, based on the exergy analysis results, optimal engine performance is obtained at 80% of full load in presence of B90 and B100 fuels. The optimum values of exergy and energy efficiencies are in the range of 25–30% of full load, which is the same as the calculated range obtained from mathematical modeling.

Size-dependent analysis of FG-CNTRC microplates based on modified strain gradient elasticity theory

Abstract: We present in this study a size-dependent computational approach based on the modified strain gradient theory (MSGT) and higher-order shear deformation theory for static bending and free vibration analyses of functionally graded carbon nanotube-reinforced composite (FG-CNTRC) microplates. Three material length scale parameters (MLSPs) are taken into account in MSGT to capture size effects of microplate behavior. The effective material properties of FG-CNTRC microplates are obtained by an extended rule of mixture. Four types of carbon nanotube distributions, which are either uniform or functionally graded (FG) through the plate thickness, are considered. The governing equations are derived from the principle of virtual work and are then solved by isogeometric analysis (IGA). The IGA is suitable for a numerical implementation of the size-dependent models since it requires higher-order gradients in the weak form. The inclusion of geometrical parameters, boundary conditions, distributed types of carbon nanotube and material length scale parameters are studied to evaluate the displacement and natural frequency of FG-CNTRC microplates. In addition, the present size-dependent model can be retrieved into the modified couple stress model or classical model when a few MLSPs are ignored.

Crowdsourcing a Large Corpus of Clickbait on Twitter

Abstract: Clickbait has become a nuisance on social media. To address the urging task of clickbait detection, we constructed a new corpus of 38,517 annotated Twitter tweets, the Webis Clickbait Corpus 2017. To avoid biases in terms of publisher and topic, tweets were sampled from the top 27 most retweeted news publishers, covering a period of 150 days. Each tweet has been annotated on 4-point scale by five annotators recruited at Amazon’s Mechanical Turk. The corpus has been employed to evaluate 12 clickbait detectors submitted to the Clickbait Challenge 2017. Download: https://webis.de/data/webis-clickbait-17.html Challenge: https://clickbait-challenge.org

Rasta: A Cipher with Low ANDdepth and Few ANDs per Bit

Abstract: Recent developments in multi party computation (MPC) and fully homomorphic encryption (FHE) promoted the design and analysis of symmetric cryptographic schemes that minimize multiplications in one way or another. In this paper, we propose with Rastaa design strategy for symmetric encryption that has ANDdepth d and at the same time only needs d ANDs per encrypted bit. Even for very low values of d between 2 and 6 we can give strong evidence that attacks may not exist. This contributes to a better understanding of the limits of what concrete symmetric-key constructions can theoretically achieve with respect to AND-related metrics, and is to the best of our knowledge the first attempt that minimizes both metrics simultaneously. Furthermore, we can give evidence that for choices of d between 4 and 6 the resulting implementation properties may well be competitive by testing our construction in the use-case of removing the large ciphertext-expansion when using the BGV scheme.

Spatial Updating and Simulator Sickness During Steering and Jumping in Immersive Virtual Environments

Abstract: Many recent head-mounted display applications and games implement a range-restricted variant of teleportation for exploring virtual environments This travel metaphor referred to as jumping only allows to teleport to locations in the currently visible part of the scene In this paper, we present a formal description and classification scheme for teleportation techniques and its application to the classification of jumping Furthermore, we present the results of a user study (N=24) that compared jumping to the more conventional steering with respect to spatial updating and simulator sickness Our results show that despite significantly faster travel times during jumping, a majority of participants (75%) achieved similar spatial updating accuracies in both conditions (mean difference 002°, =505° In addition, jumping induced significantly less simulator sickness, which altogether justifies it as an alternative to steering for the exploration of immersive virtual environments However, application developers should be aware that spatial updating during jumping may be impaired for individuals