Separating Hate Speech and Offensive Language Classes via Adversarial Debiasing

Research to tackle hate speech plaguing online media has made strides in providing solutions, analyzing bias and curating data. A challenging problem is ambiguity between hate speech and offensive language, causing low performance both overall and specifically for the hate speech class. It can be argued that misclassifying actual hate speech content as merely offensive can lead to further harm against targeted groups. In our work, we mitigate this potentially harmful phenomenon by proposing an adversarial debiasing method to separate the two classes. We show that our method works for English, Arabic German and Hindi, plus in a multilingual setting, improving performance over baselines

Development of a PIGE-Detection System for in-situ Inspection and Quality Assurance in the Evolution of Fast Rotating Parts in High Temperature Environment Manufactured from TiAl

Intermetallic γ-titanium aluminides are a promising material in high temperature technologies. Their high specific strength at temperatures above 700°C offers the possibility for their use as components of aerospace and automotive industries. With a specific weight of 50% of that of the widely used Ni-based superalloys TiAl is very suitable as material for fast rotating parts like turbine blades in aircraft engines and land based power stations or turbocharger rotors. Thus lower mechanical stresses and a reduced fuel consumption and CO2-emission are expected. To overcome the insufficient oxidation protection the halogen effect offers an innovative way. After surface doping using F-implantation or liquid phase-treatment with an F-containing solution and subsequent oxidation at high temperatures the formation of a protective alumina scale can be achieved. By using non-destructive ion beam analyses (PIGE, RBS) F was found at the metal/oxide interface. For analysis of large scale components a new vacuum chamber at the IKF was installed and became operative. With this prototype of in-situ quality assurance system for the F-doping of manufactured parts from TiAl some performance test measurements were done and presented in this paper.Received: 01 March 2013; Revised: 24 April 2013; Accepted: 25 April 201

Development of a PIGE-Detection System for In-situ Inspection and Quality Assurance in the Evolution of Fast Rotating Parts in High Temperature Environment Manufactured From TiAl

Intermetallic γ-titanium aluminides are a promising material in high temperature technologies. Their high specific strength at temperatures above 700°C offers the possibility for their use as components of aerospace and automotive industries. With a specific weight of 50% of that of the widely used Ni-based superalloys TiAl is very suitable as material for fast rotating parts like turbine blades in aircraft engines and land based power stations or turbocharger rotors. Thus lower mechanical stresses and a reduced fuel consumption and CO2-emission are expected. To overcome the insufficient oxidation protection the halogen effect offers an innovative way. After surface doping using F-implantation or liquid phase-treatment with an F-containing solution and subsequent oxidation at high temperatures the formation of a protective alumina scale can be achieved. By using non-destructive ion beam analyses (PIGE, RBS) F was found at the metal/oxide interface. For analysis of large scale components a new vacuum chamber at the IKF was installed and became operative. With this prototype of in-situ quality assurance system for the F-doping of manufactured parts from TiAl some performance test measurements were done and presented in this paper.Received: 01 March 2013; Revised: 24 April 2013; Accepted: 25 April 201

Accelerated reliability testing of articulated cable bend restrictor for offshore wind applications

PublishedFinal report.Power cable failures for offshore marine energy applications are a growing concern since experience from offshore wind has shown repeated failures of inter-array and export cables. These failures may be mitigated by dedicated cable protection systems, such as bend restrictors. This study presents the rationale and the results for accelerated reliability tests of an articulated bend restrictor. The tests are a collaborative effort between the University of Exeter, CPNL Engineering and NSW, supported by the EU Marinet Programme. The tests have been carried out at full - scale and exposed the static submarine power cable – bend restrictor specimen to mechanical load regimes exceeding the allowable design loads in order to provoke accelerated wear and component failures. The tested load cases combined cyclic bending motions with oscillating tensile forces. A range of acceleration factors have been applied in respect to the 1:50 years load case, subjecting each of the three restrictor samples to 25,000 bending cycles (50,000 tensile cycles). The static power cable was also loaded beyond its intended use, testing the worst case scenario of repeated dynamic loading, purposely inflicting failure modes for investigation. Throughout the test the static submarine power cable sustained over 77,000 bending cycles (154,000 tensile cycles). The test demonstrated the integrity of the cable protection system with quantified wear rates obtained through 3D scanning of the individual shells. The static power cable also showed a high reliability level. None of the failure modes, mainly fatigue cracks and fretting, identified by cable dissection would have caused direct loss of service. The observed failure modes could also be predicted through numerical load analysis, giving confidence in the utilised mechanical modelling and cross - sectional analysis for dynamic applications. Overall the study shows how dedicated collaborative component testing can make an important contribution to quantify and validate component behaviour in challenging offshore operating environments.The work described in this publication has received support from MARINET, a European Community - Research Infrastructure Action under the FP7 “Capacities” Specific Programme

A Descent Method for Equality and Inequality Constrained Multiobjective Optimization Problems

In this article we propose a descent method for equality and inequality constrained multiobjective optimization problems (MOPs) which generalizes the steepest descent method for unconstrained MOPs by Fliege and Svaiter to constrained problems by using two active set strategies. Under some regularity assumptions on the problem, we show that accumulation points of our descent method satisfy a necessary condition for local Pareto optimality. Finally, we show the typical behavior of our method in a numerical example

Kinetic modelling of methanol synthesis over commercial catalysts: A critical assessment

Kinetic modelling of methanol synthesis over commercial catalysts is of high importance for reactor and process design. Literature kinetic models were implemented and systematically discussed against a newly developed kinetic model based on published kinetic data. Deviations in the sensitivities of the kinetic models were explained by means of the experimentally covered parameter range. The simulation results proved that an extrapolation of the working range of the kinetic models can lead towards significant simulation errors especially with regard to pressure, stoichiometric number and CO/CO$_{2}$-ratio considerably limiting the applicability of kinetic models frequently applied in scientific literature. Therefore, the validated data range for kinetic models should be considered when detailed reactor simulations are carried out. With regard to Power-to-Methanol processes special attention should be drawn towards the rate limiting effect of water at high CO$_{2}$ contents in the syngas. Moreover, it was shown that kinetic models based on data measured over outdated catalysts show significantly lower activity than those derived from state-of-the-art catalysts and should therefore be applied with caution for reactor and process simulations. The plausible behavior of the herein proposed kinetic model was demonstrated by a systematic comparison towards established kinetic approaches within both, an ideal kinetic reactor and an industrial steam cooled tubular reactor. Relative to the state-of-the-art kinetic models it was proven that the herein proposed kinetic model can be applied over the complete industrially relevant working range for methanol synthesis

Basal forebrain integrity and cognitive memory profile in healthy aging

Age-related dysfunctions in cholinergic and dopaminergic neuromodulation are assumed to contribute to age-associated impairment of explicit memory. Both neurotransmitters also modulate attention, working memory, and processing speed. To date, in vivo evidence linking structural age-related changes in these neuromodulatory systems to dysfunction within or across these cognitive domains remains scarce. Using a factor analytical approach in a cross-sectional study including 86 healthy older (aged 55 to 83 years) and 24 young (aged 18 to 30 years) adults, we assessed the relationship between structural integrity-as measured by magnetization transfer ratio (MTR)-of the substantia nigra/ventral tegmental area (SN/VTA), main origin of dopaminergic projections, basal forebrain (major origin of cortical cholinergic projections), frontal white matter (FWM), and hippocampus to neuro psychological and psychosocial scores. Basal forebrain MTR and FWM changes correlated with a factor combining verbal learning and memory and working memory and, as indicated by measures of diffusion, were most likely due to vascular pathology. These findings suggest that frontal white matter integrity and cholinergic neuromodulation provide clues as to why age-related cognitive decline is often correlated across cognitive domains. (C) 2009 Elsevier B.V. All rights reserved

Influence of measurement uncertainty on machine learning results demonstrated for a smart gas sensor

Humans spend most of their lives indoors, so indoor air quality (IAQ) plays a key role in human health. Thus, human health is seriously threatened by indoor air pollution, which leads to 3.8 ×106 deaths annually, according to the World Health Organization (WHO). With the ongoing improvement in life quality, IAQ monitoring has become an important concern for researchers. However, in machine learning (ML), measurement uncertainty, which is critical in hazardous gas detection, is usually only estimated using cross-validation and is not directly addressed, and this will be the main focus of this paper. Gas concentration can be determined by using gas sensors in temperature-cycled operation (TCO) and ML on the measured logarithmic resistance of the sensor. This contribution focuses on formaldehyde as one of the most relevant carcinogenic gases indoors and on the sum of volatile organic compounds (VOCs), i.e., acetone, ethanol, formaldehyde, and toluene, measured in the data set as an indicator for IAQ. As gas concentrations are continuous quantities, regression must be used. Thus, a previously published uncertainty-aware automated ML toolbox (UA-AMLT) for classification is extended for regression by introducing an uncertainty-aware partial least squares regression (PLSR) algorithm. The uncertainty propagation of the UA-AMLT is based on the principles described in the Guide to the Expression of Uncertainty in Measurement (GUM) and its supplements. Two different use cases are considered for investigating the influence on ML results in this contribution, namely model training with raw data and with data that are manipulated by adding artificially generated white Gaussian or uniform noise to simulate increased data uncertainty, respectively. One of the benefits of this approach is to obtain a better understanding of where the overall system should be improved. This can be achieved by either improving the trained ML model or using a sensor with higher precision. Finally, an increase in robustness against random noise by training a model with noisy data is demonstrated.</p

Multi-objective Optimization by Uncrowded Hypervolume Gradient Ascent

Evolutionary algorithms (EAs) are the preferred method for solving black-box multi-objective optimization problems, but when gradients of the objective functions are available, it is not straightforward to exploit these efficiently. By contrast, gradient-based optimization is well-established for single-objective optimization. A single-objective reformulation of the multi-objective problem could therefore offer a solution. Of particular interest to this end is the recently introduced uncrowded hypervolume (UHV) indicator, which takes into account dominated solutions. In this work, we show that the gradient of the UHV can often be computed, which allows for a direct application of gradient ascent algorithms. We compare this new approach with two EAs for UHV optimization as well as with one gradient-based algorithm for optimizing the well-established hypervolume. On several bi-objective benchmarks, we find that gradient-based algorithms outperform the tested EAs by obtaining a better hypervolume with fewer evaluations whenever exact gradients of the multiple objective functions are available and in case of small evaluation budgets. For larger budgets, however, EAs perform similarly or better. We further find that, when finite differences are used to approximate the gradients of the multiple objectives, our new gradient-based algorithm is still competitive with EAs in most considered benchmarks. Implementations are available at https://github.com/scmaree/uncrowded-hypervolume.Comment: T.M.D. and S.C.M. contributed equally. The final authenticated version is available in the conference proceedings of Parallel Problem Solving from Nature - PPSN XVI. Changes in new version: removed statement about Pareto compliance in abstract; added related work; corrected minor mistake