bluuebunny/crossref_metadata_2025_split huggingface dataset:
Algorithmic fairness datasets: the story so far
Authors: Fabris Alessandro , Messina Stefano , Silvello Gianmaria , Susto Gian Antonio 
Published: September 2022

AbstractData-driven algorithms are studied and deployed in diverse domains to support critical decisions, directly impacting people’s well-being. As a result, a growing community of researchers has been investigating the equity of existing algorithms and proposing novel ones, advancing the understanding of risks and opportunities of automated decision-making for historically disadvantaged populations. Progress in fair machine learning and equitable algorithm design hinges on data, which can be appropriately used only if adequately documented. Unfortunately, the algorithmic fairness community, as a whole, suffers from a collective data documentation debt caused by a lack of information on specific resources (opacity) and scatteredness of available information (sparsity). In this work, we target this data documentation debt by surveying over two hundred datasets employed in algorithmic fairness research, and producing standardized and searchable documentation for each of them. Moreover we rigorously identify the three most popular fairness datasets, namely Adult, COMPAS, and German Credit, for which we compile in-depth documentation. This unifying documentation effort supports multiple contributions. Firstly, we summarize the merits and limitations of Adult, COMPAS, and German Credit, adding to and unifying recent scholarship, calling into question their suitability as general-purpose fairness benchmarks. Secondly, we document hundreds of available alternatives, annotating their domain and supported fairness tasks, along with additional properties of interest for fairness practitioners and researchers, including their format, cardinality, and the sensitive attributes they encode. We summarize this information, zooming in on the tasks, domains, and roles of these resources. Finally, we analyze these datasets from the perspective of five important data curation topics: anonymization, consent, inclusivity, labeling of sensitive attributes, and transparency. We discuss different approaches and levels of attention to these topics, making them tangible, and distill them into a set of best practices for the curation of novel resources.
An external stability audit framework to test the validity of personality prediction in AI hiring
Authors: Rhea Alene K. , Markey Kelsey , D’Arinzo Lauren , Schellmann Hilke , Sloane Mona , Squires Paul , Arif Khan Falaah , Stoyanovich Julia 
Published: September 2022

AbstractAutomated hiring systems are among the fastest-developing of all high-stakes AI systems. Among these are algorithmic personality tests that use insights from psychometric testing, and promise to surface personality traits indicative of future success based on job seekers’ resumes or social media profiles. We interrogate the validity of such systems using stability of the outputs they produce, noting that reliability is a necessary, but not a sufficient, condition for validity. Crucially, rather than challenging or affirming the assumptions made in psychometric testing — that personality is a meaningful and measurable construct, and that personality traits are indicative of future success on the job — we frame our audit methodology around testing the underlying assumptions made by the vendors of the algorithmic personality tests themselves. Our main contribution is the development of a socio-technical framework for auditing the stability of algorithmic systems. This contribution is supplemented with an open-source software library that implements the technical components of the audit, and can be used to conduct similar stability audits of algorithmic systems. We instantiate our framework with the audit of two real-world personality prediction systems, namely, Humantic AI and Crystal. The application of our audit framework demonstrates that both these systems show substantial instability with respect to key facets of measurement, and hence cannot be considered valid testing instruments.
Assessment of the environmental drivers of European glass eel (Anguilla anguilla) recruitment in transitional waters
Authors: O’Leary Ciara , Healy Sarah , Cruikshanks Robert , Kelly Karen , Gargan Patrick 
Published: September 2022

AbstractThe European eel (Anguilla anguilla) has undergone an unprecedented population decline since the 1980s, with current recruitment levels fluctuating from 3 to 15% of historical levels for the last 20 years. Monitoring of glass eels and elvers as 0 + recruitment is an essential step in helping to understand the trend in recruitment and to better quantify the current recruitment time series. Two locations within the Shannon estuary on the west coast of Ireland were monitored for glass eel recruitment from January to April in 2017 and 2018. This study used a generalised linear mixed model to examine a range of environmental variables impacting on glass eel abundance in transitional waters. Results found that water temperature and moon phase were the most important variables. Tidal height and cloud cover also influenced the abundance of glass eels but to a lesser extent. This study found that focussing survey efforts on nights around the full moon when water temperatures exceed 5℃ will allow a catch which is representative of the population in an estuary. Glass eel monitoring needs a long-term sampling plan in order to account for annual fluctuations apparent in glass eel recruitment.
Radon (222Rn) as tracer for submarine groundwater discharge investigation—limitations of the approach at shallow wind-exposed coastal settings
Authors: Schubert Michael , Scholten Jan , Kreuzburg Matthias , Petermann Eric , de Paiva Mariele Lopes , Köhler Dennis , Liebetrau Volker , Rapaglia John , Schlüter Michael 
Published: September 2022

AbstractMapping radon (222Rn) distribution patterns in the coastal sea is a widely applied method for localizing and quantifying submarine groundwater discharge (SGD). While the literature reports a wide range of successful case studies, methodical problems that might occur in shallow wind-exposed coastal settings are generally neglected. This paper evaluates causes and effects that resulted in a failure of the radon approach at a distinct shallow wind-exposed location in the Baltic Sea. Based on a simple radon mass balance model, we discuss the effect of both wind speed and wind direction as causal for this failure. We show that at coastal settings, which are dominated by gentle submarine slopes and shallow waters, both parameters have severe impact on coastal radon distribution patterns, thus impeding their use for SGD investigation. In such cases, the radon approach needs necessarily to allow for the impact of wind speed and wind direction not only during but also prior to the field campaign.
Governing Through Ignorance: Swedish Authorities’ Treatment of Detained and Non-deported Migrants during the COVID-19 Pandemic
Authors: Lindberg Annika , Lundberg Anna , Rundqvist Elisabet , Häythiö Sofia 
Published: September 2022

AbstractTensions between migration enforcement and migrants’ health and rights have gained renewed urgency during the COVID-19 pandemic. This article critically analyses how the pandemic has affected detained and deportable people in Sweden. Building on an activist methodological approach and collaboration, based on a survey conducted inside Swedish detention centres during the pandemic and the authors’ research and activist engagement with migrants who are detained or legally stranded in Sweden, we argue that migration authorities’ inadequate measures to protect detained and deportable people during the pandemic is a case of governance through ignorance enabled by structural racism. The article traces how this ignorance operates on a structural, institutional and micro-level, enabling public disregard and political irresponsibility for the harmful effects of migration enforcement. A broader aim of the article is to challenge the structural, societal and epistemic ignorance of the conditions for detained and deportable persons and to contribute to political change.
Explaining the Association Between Fetal Growth and Childhood ADHD Symptoms: Cross-cohort Replication
Authors: Dooley Niamh , Healy Colm , Brannigan Ross , Cotter David , Clarke Mary , Cannon Mary 
Published: September 2022

AbstractThe association between restricted fetal growth and symptoms of attention deficit/hyperactivity disorder (ADHD) in childhood is well-replicated and robust. However, fetal growth is determined by many prenatal factors and associations with mental health may be confounded by familial and social context. In this study, we sought to quantify the relative contributions of prenatal factors and familial confounds to the association between fetal growth and ADHD symptoms. Two independent cohorts were analyzed, the Adolescent Brain Cognitive Development study (ABCD; United States) and the Growing Up in Ireland (GUI) study. ADHD symptoms were measured by the Child Behavior Checklist (ABCD) and the Strengths & Difficulties questionnaire (GUI) at age 9–10. Using sequential regression models, we assessed the change-in-association between fetal growth and ADHD symptoms after controlling for sex, familial factors (socioeconomic/demographic factors & family psychiatric history) and prenatal factors (pregnancy complications & maternal substance-use during pregnancy). Converging findings from cohorts suggested that over a quarter of the association between fetal growth and ADHD symptoms is attributable to familial confounds. The degree to which the association was explained by prenatal factors differed by cohort—pregnancy complications explained a larger proportion of the effect in ABCD (7.9%) than GUI (2.7%), and maternal substance-use explained a larger proportion of the effect in GUI (22.7%) compared to ABCD (4.8%). Different explanations of the fetal growth-ADHD association across cohorts suggests cohort-specific, and potentially nationally-specific, risk factors for fetal growth and related neurodevelopmental outcomes. The evidence suggests early prevention of ADHD in Ireland should focus on minimizing maternal smoking during pregnancy. In the US, prevention and treatment of pregnancy complications are highlighted as viable targets for intervention.
Do You See What I See? Longitudinal Associations Between Mothers’ and Adolescents’ Perceptions of Their Relationship and Adolescent Internalizing Symptoms
Authors: Nelemans Stefanie A. , Mastrotheodoros Stefanos , Çiftçi Leyla , Meeus Wim , Branje Susan 
Published: September 2022

AbstractThis 6-year community study examined how discrepancies in mothers’ and adolescents’ perceptions of their relationship were longitudinally associated with adolescent internalizing symptoms, and vice versa. 497 adolescents (57% boys, Mage T1 = 13.03, SDage = 0.46) and their mothers reported in 6 annual waves on conflict and warmth in the mother-adolescent relationship and adolescents reported on their depressive and Generalized Anxiety Disorder (GAD) symptoms. Latent Congruence Models suggested that both adolescent depressive and GAD symptoms significantly predicted higher levels of conflict as well as stronger discrepancies in perceptions of conflict 1-year later. In turn, higher levels of conflict significantly predicted both adolescent depressive and GAD symptoms 1-year later. For warmth, lower levels significantly predicted adolescent depressive symptoms 1-year later. Concluding, these findings suggest (1) more systematic evidence for longitudinal associations between conflict than warmth in the mother-adolescent relationship and adolescent internalizing symptoms; (2) support for a transactional model, including support for both interpersonal scar or symptom-driven effects (concerning both levels of and mother-adolescent discrepancies in conflict) and interpersonal risk or relationship-driven effects (concerning levels of both conflict and warmth); (3) longitudinal effects from adolescent internalizing symptoms to mother-adolescent discrepancies, but not vice versa; and (4) strong consistency in patterns of findings across both adolescent depressive and GAD symptoms, with few differential longitudinal associations with aspects of mother-adolescent relationship quality. Thereby, this study provides a more nuanced understanding of the direction of effects between adolescent internalizing symptoms and both levels of and discrepancies in mothers’ and adolescents’ perceptions of their relationship.
A context-aware unsupervised predictive maintenance solution for fleet management
Authors: Giannoulidis Apostolos , Gounaris Anastasios 
Published: September 2022

AbstractWe deal with the problem of predictive maintenance (PdM) in a vehicle fleet management setting following an unsupervised streaming anomaly detection approach. We investigate a variety of unsupervised methods for anomaly detection, such as proximity-based, hybrid (statistical and proximity-based) and transformers. The proposed methods can properly model the context in which each member of the fleet operates. In our case, the context is both crucial for effective anomaly detection and volatile, which calls for streaming solutions that take into account only the recent values. We propose two novel techniques, a 2-stage proximity-based one and context-aware transformers along with advanced thresholding. In addition, to allow for testing PdM techniques for vehicle fleets in a fair and reproducible manner, we build a new fleet-like benchmarking dataset based on an existing dataset of turbofan simulations. Our evaluation results show that our proposals reduce the maintenance costs compared to existing solutions.
Optimisation-driven design to explore and exploit the process–structure–property–performance linkages in digital manufacturing
Authors: Flores Ituarte Iñigo , Panicker Suraj , Nagarajan Hari P. N. , Coatanea Eric , Rosen David W. 
Published: September 2022

AbstractAn intelligent manufacturing paradigm requires material systems, manufacturing systems, and design engineering to be better connected. Surrogate models are used to couple product-design choices with manufacturing process variables and material systems, hence, to connect and capture knowledge and embed intelligence in the system. Later, optimisation-driven design provides the ability to enhance the human cognitive abilities in decision-making in complex systems. This research proposes a multidisciplinary design optimisation problem to explore and exploit the interactions between different engineering disciplines using a socket prosthetic device as a case study. The originality of this research is in the conceptualisation of a computer-aided expert system capable of exploring process–structure–property–performance linkages in digital manufacturing. Thus, trade-off exploration and optimisation are enabled of competing objectives, including prosthetic socket mass, manufacturing time, and performance-tailored socket stiffness for patient comfort. The material system is modelled by experimental characterisation—the manufacturing time by computer simulations, and the product-design subsystem is simulated using a finite element analysis (FEA) surrogate model. We used polynomial surface response-based surrogate models and a Bayesian Network for design space exploration at the embodiment design stage. Next, at detail design, a gradient descent algorithm-based optimisation exploits the results using desirability functions to isolate Pareto non-dominated solutions. This work demonstrates how advanced engineering design synthesis methods can enhance designers’ cognitive ability to explore and exploit multiple disciplines concurrently and improve overall system performance, thus paving the way for the next generation of computer systems with highly intertwined material, digital design and manufacturing workflows.
Graphical abstract
Comparison of Semi-autonomous Mobile Robot Control Strategies in Presence of Large Delay Fluctuation
Authors: Seppänen Alvari , Vepsäläinen Jari , Ojala Risto , Tammi Kari 
Published: September 2022

AbstractWe propose semi-autonomous control strategies to assist in the teleoperation of mobile robots under unstable communication conditions. A short-term autonomous control system is the assistance in the semi-autonomous control strategies, when the teleoperation is compromised. The short-term autonomous control comprises of lateral and longitudinal functions. The lateral control is based on an artificial potential field method where obstacles are repulsive, and a route is attractive. LiDAR-based artificial potential field methods are well studied. We present a novel artificial potential field method based on color and depth images. Benefit of a camera system compared to a LiDAR is that a camera detects color, is cheaper, and does not have moving parts. Moreover, utilization of active sensors is not desired in the particle accelerator environment. A set of experiments with a robot prototype are carried out to validate this system. The experiments are carried out in an environment which mimics the accelerator tunnel environment. The difficulty of the teleoperation is altered with obstacles. Fully manual and autonomous control are compared with the proposed semi-autonomous control strategies. The results show that the teleoperation is improved with autonomous, delay-dependent, and control-dependent assist compared to the fully manual control. Based on the operation time, control-dependent assist performed the best, reducing the time by 12% on the tunnel section with most obstacles. The presented system can be easily applied to common industrial robots operating e.g. in warehouses or factories due to hardware simplicity and light computational demand.
Polish Criminal Liability of the UAV Operator in Connection with the Unauthorized Flight in Operations Area of Fire Services in the Context of the Possibility of the U-Space Using
Authors: Balcerzak Tomasz , Sieradzka Małgorzata 
Published: September 2022

AbstractThe events of recent weeks have reminded us how difficult it is to fight the element of fire. Knowing where the fire has started allows emergency services to react faster and more efficiently. That is why unmanned aerial vehicles are being used more and more often. A drone with a thermal imaging camera locates fire outbreaks from the air. The drone will not only reach places that are difficult to reach, but also record an image from a wide area and—importantly—allow you to assess the extent of damage and fire extinguishing activities in real time. The unmanned aerial vehicle with a thermal imaging camera installed, despite the clouds of smoke and poor visibility, registers where the fire is located and in which direction the fire is spreading. The obtained data, along with the exact coordinates of the places that are on fire, are forwarded to the fire brigade. On this basis, firefighters can react quickly and move to a specific area to extinguish a fire. The analysis of the temperature of the indicated area also translated into the safety of the services participating in the fire extinguishing operation. This refers to issues in the area of public safety and privacy. UAV, under the international, regional and national regime of aviation law, are considered as aircraft. Since there is no framework existing in connection with the unauthorized flight in operations area of fire services in the context of the possibility of the U-space using, this paper will focus on this analysis. The paper’s conclusion will address the proposed recommendations for the UAV and aviation industries to consider as potential solution- implementation of the U-space concept. This idea, apart from the regulations of aviation law and other branches of law seems to be relevant for sanctioning the use of the airspace in fire services application.
Vector Field based Control of Quadrotor UAVs for Wildfire Boundary Monitoring
Authors: Feng Licheng , Katupitiya Jay 
Published: September 2022

AbstractAccurate real-time information about an ongoing wildfire event is important for realizing effective and safe wildfire fighting. This paper is intended to solve the problem of guiding Unmanned Air Vehicles (UAVs) equipped with onboard cameras to monitor dynamic wildfire boundaries. According to whether the prior knowledge of the wildfire boundary is available or not, we propose a model-based vector field and a model-free vector field for UAV guidance. By describing the wildfire boundary with a zero level set function, the propagation of the wildfire boundary is modeled with the Hamilton-Jacobi equation. If the prior knowledge of the boundary is available, the typical radial basis function thin-plate spline is adopted to approximate the wildfire boundary and predicts its propagation. Then a 3D analytical vector field is constructed for an implicit function representing the wildfire boundary. If only partial observation of the wildfire boundary within the UAV’s field of view is available, the horizontal error between the UAV and its sensed segment of wildfire boundary and the vertical error between the UAV and the desired altitude are utilized to construct a 3D distance error based vector field, directly. To guide the UAV to converge to and patrol along the advancing wildfire boundary, the complex nonlinear dynamics of the UAV is exploited with differential flatness and incorporated with the above mentioned vector fields to design a nonlinear geometric controller. Computer simulations have been conducted to evaluate the performance of the proposed 3D vector field based controllers with both synthetic and real data, and simulation results demonstrate that the proposed algorithms can be effective methods to monitor the advancing wildfire boundaries.
A Modified Michael’s Selection Theorem with Application to Generalized Nash Equilibrium Problem
Authors: Castellani Marco , Giuli Massimiliano 
Published: September 2022

AbstractThis paper provides a sufficient condition for the existence of solutions for generalized Nash equilibrium problems in the infinite-dimensional setting and with a countable (possibly infinite) number of players. The result has been achieved as a consequence of a modified version of Michael’s selection theorem that works even when the range space is not metrizable and the set-valued map has not closed values.
Osteoporosis and risk of fracture: reference class problems are real
Authors: Binney Nicholas 
Published: September 2022

AbstractElselijn Kingma argues that Christopher Boorse’s biostatistical theory does not show how the reference classes it uses—namely, age groups of a sex of a species—are objective and naturalistic. Boorse has replied that this objection is of no concern, because there are no examples of clinicians’ choosing to use reference classes other than the ones he suggests. Boorse argues that clinicians use the reference classes they do because these reflect the natural classes of organisms to which their patients belong. Drawing on a thorough exploration of how the disease osteoporosis is defined in adults, I argue that clinicians do indeed make choices about which reference classes to use in diagnosis. Clinicians use young adult reference classes to diagnose osteoporosis in elderly patients. They also use young female reference classes to diagnose osteoporosis in elderly males. Clinicians adjust their reference classes so that the diagnosis of osteoporosis reflects a person’s risk of sustaining a fragility fracture. The ethical intuition that people with the same risk of fracture should receive the same diagnosis overwhelms the naturalistic intuition that reference classes should reflect natural classes of organisms of uniform functional design. Clinicians construct a variety of reference class types, including pathological reference classes and epidemiological population-specific reference classes, to serve this ethical intuition. I show how clinicians use several reference classes at once so that they can more accurately predict risk of fracture. Ultimately, the reference classes chosen and used in medical practice are quite different from those proposed in naturalistic philosophy of medicine.