Did You Do Your Homework? Raising Awareness on Software Fairness and Discrimination

Machine Learning is a vital part of various modern day decision making software. At the same time, it has shown to exhibit bias, which can cause an unjust treatment of individuals and population groups. One method to achieve fairness in machine learning software is to provide individuals with the same degree of benefit, regardless of sensitive attributes (e.g., students receive the same grade, independent of their sex or race). However, there can be other attributes that one might want to discriminate against (e.g., students with homework should receive higher grades). We will call such attributes anti-protected attributes. When reducing the bias of machine learning software, one risks the loss of discriminatory behaviour of anti-protected attributes. To combat this, we use grid search to show that machine learning software can be debiased (e.g., reduce gender bias) while also improving the ability to discriminate against anti-protected attributes

The Effect of Offspring Population Size on NSGA-II: A Preliminary Study

Non-Dominated Sorting Genetic Algorithm (NSGA-II) is one of the most popular Multi-Objective Evolutionary Algorithms (MOEA) and has been applied to a large range of problems. Previous studies have shown that parameter tuning can improve NSGA-II performance. However, the tuning of the offspring population size, which guides the exploration-exploitation trade-off in NSGA-II, has been overlooked so far. Previous work has generally used the population size as the default offspring population size for NSGA-II. We therefore investigate the impact of offspring population size on the performance of NSGA-II. We carry out an empirical study by comparing the effectiveness of three configurations vs. the default NSGA-II configuration on six optimization problems based on four Pareto front quality indicators and statistical tests. Our findings show that the performance of NSGA-II can be improved by reducing the offspring population size and in turn increasing the number of generations. This leads to similar or statistically significant better results than those obtained by using the default NSGA-II configuration in 92% of the experiments performed

A Survey of Performance Optimization for Mobile Applications

Nowadays there is a mobile application for almost everything a user may think of, ranging from paying bills and gathering information to playing games and watching movies. In order to ensure user satisfaction and success of applications, it is important to provide high performant applications. This is particularly important for resource constraint systems such as mobile devices. Thereby, non-functional performance characteristics, such as energy and memory consumption, play an important role for user satisfaction. This paper provides a comprehensive survey of non-functional performance optimization for Android applications. We collected 155 unique publications, published between 2008 and 2020, that focus on the optimization of non-functional performance of mobile applications. We target our search at four performance characteristics, in particular: responsiveness, launch time, memory and energy consumption. For each performance characteristic, we categorize optimization approaches based on the method used in the corresponding publications. Furthermore, we identify research gaps in the literature for future work

The January 2006 low ozone event over the UK

International audienceA record low total ozone column of 177 DU was observed at Reading, UK, on 19 January 2006. Low ozone values were also recorded at other stations in the British Isles and North West Europe on, and around, this date. Hemispheric maps of total ozone from the World Meteorological Organisation (WMO) Ozone Mapping Centre also show the evolution of this ozone minimum from 15?20 January 2006 over North West Europe. Ozonesonde measurements made at Lerwick, UK, show that ozone mixing ratios in the mid-stratosphere on 18 January are around 1?2 ppmv lower than both climatology and observations made one and two weeks prior to this date. In addition, ozone mixing ratios in the UTLS region were also noticeably reduced on 18 January. Analysis of the ozonesonde observations indicate that the mid-stratosphere ozone accounts for around a third of the reduction in total ozone column measurements while the UTLS ozone values account for two thirds of the depletion. It is evident from the ozonesonde data that ozone loss is occuring at two distinct vertical regions. Met Office analyses indicate that stratospheric polar vortex temperatures were cold enough for Polar Stratospheric Cloud (PSC) formation during 14 days in January prior to the low ozone event on 19 January. The presence of PSCs is confirmed by observations from the Scanning Imaging Absorption spectroMeter for Atmospheric CartograpHY (SCIAMACHY). As a consequence of a stratospheric sudden warming that was in progress during January 2006, the polar vortex was shifted southwards over northwest Europe. This includes a period from 16 to 19 January where PSCs were present in the vortex over the UK. Throughout most of January suitable conditions were present for ozone destruction by heterogenous chemistry within the polar vortex. Evidence from Lerwick and Sodankylä ozonesonde profiles, and maps of Ertel's potential vorticity calculated from Met Office analyses, strongly suggests that the air inside the stratospheric vortex was poor in ozone for at least one week prior to 18 January. It is also possible that local chemical destruction of stratospheric ozone further contributed to the record low ozone observed at Reading. A closer examination of the WMO total ozone maps shows that the daily minima are often of synoptic, rather than planetary, scale. This therefore suggests a tropospheric, rather than stratospheric, mechanism for the ozone minima. Moderate total ozone depletion is commonly observed in the northern hemisphere middle and high latitude winter. This depletion is related to the lifting of the tropopause associated with the presence of an upper troposphere/lower stratosphere anticyclone. We show a strong link between the ozone minima in the WMO maps and 100 hPa geopotential height from Met Office analyses, and therefore it appears that this may also be a plausible mechanism for the record low ozone column that is observed. Back trajectories calculated by the Met Office NAME III model show that air parcels in the mid-stratosphere do arrive over the British Isles on 19 January via the polar vortex. The NAME III model results also show that air parcels near the tropopause arrive from low latitudes and are transported anticyclonically. Therefore this strongly suggests that the record low ozone values are due to a combination of a raised tropopause and the presence of low ozone stratospheric air aloft

Fairea: A Model Behaviour Mutation Approach to Benchmarking Bias Mitigation Methods

The increasingly wide uptake of Machine Learning (ML) has raised the significance of the problem of tackling bias (i.e., unfairness), making it a primary software engineering concern. In this paper, we introduce Fairea, a model behaviour mutation approach to benchmarking ML bias mitigation methods. We also report on a large-scale empirical study to test the effectiveness of 12 widely-studied bias mitigation methods. Our results reveal that, surprisingly, bias mitigation methods have a poor effectiveness in 49% of the cases. In particular, 15% of the mitigation cases have worse fairness-accuracy trade-offs than the baseline established by Fairea; 34% of the cases have a decrease in accuracy and an increase in bias. Fairea has been made publicly available for software engineers and researchers to evaluate their bias mitigation methods

Volcanic ash as fertiliser for the surface ocean

Iron is a key limiting micro-nutrient for marine primary productivity. It can be supplied to the ocean by atmospheric dust deposition. Volcanic ash deposition into the ocean represents another external and so far largely neglected source of iron. This study demonstrates strong evidence for natural fertilisation in the iron-limited oceanic area of the NE Pacific, induced by volcanic ash from the eruption of Kasatochi volcano in August 2008. Atmospheric and oceanic conditions were favourable to generate a massive phytoplankton bloom in the NE Pacific Ocean which for the first time strongly suggests a connection between oceanic iron-fertilisation and volcanic ash supply

A detailed view into the eruption clouds of Santiaguito volcano, Guatemala, using Doppler radar

Using Doppler radar technology we are able to show that eruptions at Santiaguito volcano, Guatemala, are comprised of multiple explosive degassing pulses occurring at a frequency of 0.2 to 0.3 Hz. The Doppler radar system was installed about 2.7 km away from the active dome on the top of Santa Maria volcano. During four days of continuous measurement 157 eruptive events were recorded. The Doppler radar data reveals a vertical uplift of the dome surface of about 50 cm immediately prior to a first degassing pulse. Particle velocities range from 10 to 15 m/s (in the line of sight of the radar). In 80% of the observed eruptions a second degassing pulse emanates from the dome with significantly higher particle velocities (20-25 m/s again line of sight) and increased echo power, which implies an increase in mass flux. We carry out numerical experiments of ballistic particle transport and calculate corresponding synthetic radar signals. These calculations show that the observations are consistent with a pulsed release of material from the dome of Santiaguito volcano

Fairness Testing: A Comprehensive Survey and Analysis of Trends

Unfair behaviors of Machine Learning (ML) software have garnered increasing attention and concern among software engineers. To tackle this issue, extensive research has been dedicated to conducting fairness testing of ML software, and this paper offers a comprehensive survey of existing studies in this field. We collect 100 papers and organize them based on the testing workflow (i.e., how to test) and testing components (i.e., what to test). Furthermore, we analyze the research focus, trends, and promising directions in the realm of fairness testing. We also identify widely-adopted datasets and open-source tools for fairness testing