TY - JOUR A1 - Levy, Jessica A1 - Mussack, Dominic A1 - Brunner, Martin A1 - Keller, Ulrich A1 - Cardoso-Leite, Pedro A1 - Fischbach, Antoine T1 - Contrasting classical and machine learning approaches in the estimation of value-added scores in large-scale educational data JF - Frontiers in psychology N2 - There is no consensus on which statistical model estimates school value-added (VA) most accurately. To date, the two most common statistical models used for the calculation of VA scores are two classical methods: linear regression and multilevel models. These models have the advantage of being relatively transparent and thus understandable for most researchers and practitioners. However, these statistical models are bound to certain assumptions (e.g., linearity) that might limit their prediction accuracy. Machine learning methods, which have yielded spectacular results in numerous fields, may be a valuable alternative to these classical models. Although big data is not new in general, it is relatively new in the realm of social sciences and education. New types of data require new data analytical approaches. Such techniques have already evolved in fields with a long tradition in crunching big data (e.g., gene technology). The objective of the present paper is to competently apply these "imported" techniques to education data, more precisely VA scores, and assess when and how they can extend or replace the classical psychometrics toolbox. The different models include linear and non-linear methods and extend classical models with the most commonly used machine learning methods (i.e., random forest, neural networks, support vector machines, and boosting). We used representative data of 3,026 students in 153 schools who took part in the standardized achievement tests of the Luxembourg School Monitoring Program in grades 1 and 3. Multilevel models outperformed classical linear and polynomial regressions, as well as different machine learning models. However, it could be observed that across all schools, school VA scores from different model types correlated highly. Yet, the percentage of disagreements as compared to multilevel models was not trivial and real-life implications for individual schools may still be dramatic depending on the model type used. Implications of these results and possible ethical concerns regarding the use of machine learning methods for decision-making in education are discussed. KW - value-added modeling KW - school effectiveness KW - machine learning KW - model KW - comparison KW - longitudinal data Y1 - 2020 U6 - https://doi.org/10.3389/fpsyg.2020.02190 SN - 1664-1078 VL - 11 PB - Frontiers Research Foundation CY - Lausanne ER - TY - JOUR A1 - Ayzel, Georgy A1 - Izhitskiy, Alexander T1 - Climate Change Impact Assessment on Freshwater Inflow into the Small Aral Sea JF - Water N2 - During the last few decades, the rapid separation of the Small Aral Sea from the isolated basin has changed its hydrological and ecological conditions tremendously. In the present study, we developed and validated the hybrid model for the Syr Darya River basin based on a combination of state-of-the-art hydrological and machine learning models. Climate change impact on freshwater inflow into the Small Aral Sea for the projection period 2007-2099 has been quantified based on the developed hybrid model and bias corrected and downscaled meteorological projections simulated by four General Circulation Models (GCM) for each of three Representative Concentration Pathway scenarios (RCP). The developed hybrid model reliably simulates freshwater inflow for the historical period with a Nash-Sutcliffe efficiency of 0.72 and a Kling-Gupta efficiency of 0.77. Results of the climate change impact assessment showed that the freshwater inflow projections produced by different GCMs are misleading by providing contradictory results for the projection period. However, we identified that the relative runoff changes are expected to be more pronounced in the case of more aggressive RCP scenarios. The simulated projections of freshwater inflow provide a basis for further assessment of climate change impacts on hydrological and ecological conditions of the Small Aral Sea in the 21st Century. KW - Small Aral Sea KW - hydrology KW - climate change KW - modeling KW - machine learning Y1 - 2019 U6 - https://doi.org/10.3390/w11112377 SN - 2073-4441 VL - 11 IS - 11 PB - MDPI CY - Basel ER - TY - JOUR A1 - Frommhold, Martin A1 - Heim, Arend A1 - Barabanov, Mikhail A1 - Maier, Franziska A1 - Mühle, Ralf-Udo A1 - Smirenski, Sergei M. A1 - Heim, Wieland T1 - Breeding habitat and nest-site selection by an obligatory "nest-cleptoparasite", the Amur Falcon Falco amurensis JF - Ecology and evolution N2 - The selection of a nest site is crucial for successful reproduction of birds. Animals which re-use or occupy nest sites constructed by other species often have limited choice. Little is known about the criteria of nest-stealing species to choose suitable nesting sites and habitats. Here, we analyze breeding-site selection of an obligatory "nest-cleptoparasite", the Amur Falcon Falco amurensis. We collected data on nest sites at Muraviovka Park in the Russian Far East, where the species breeds exclusively in nests of the Eurasian Magpie Pica pica. We sampled 117 Eurasian Magpie nests, 38 of which were occupied by Amur Falcons. Nest-specific variables were assessed, and a recently developed habitat classification map was used to derive landscape metrics. We found that Amur Falcons chose a wide range of nesting sites, but significantly preferred nests with a domed roof. Breeding pairs of Eurasian Hobby Falco subbuteo and Eurasian Magpie were often found to breed near the nest in about the same distance as neighboring Amur Falcon pairs. Additionally, the occurrence of the species was positively associated with bare soil cover, forest cover, and shrub patches within their home range and negatively with the distance to wetlands. Areas of wetlands and fallow land might be used for foraging since Amur Falcons mostly depend on an insect diet. Additionally, we found that rarely burned habitats were preferred. Overall, the effect of landscape variables on the choice of actual nest sites appeared to be rather small. We used different classification methods to predict the probability of occurrence, of which the Random forest method showed the highest accuracy. The areas determined as suitable habitat showed a high concordance with the actual nest locations. We conclude that Amur Falcons prefer to occupy newly built (domed) nests to ensure high nest quality, as well as nests surrounded by available feeding habitats. KW - cleptoparasitism KW - fire KW - habitat use KW - machine learning KW - magpie KW - nest-site selection KW - random forest Y1 - 2019 U6 - https://doi.org/10.1002/ece3.5878 SN - 2045-7758 VL - 9 IS - 24 SP - 14430 EP - 14441 PB - Wiley CY - Hoboken ER - TY - JOUR A1 - Wilksch, Moritz A1 - Abramova, Olga T1 - PyFin-sentiment BT - towards a machine-learning-based model for deriving sentiment from financial tweets JF - International journal of information management data insights N2 - Responding to the poor performance of generic automated sentiment analysis solutions on domain-specific texts, we collect a dataset of 10,000 tweets discussing the topics of finance and investing. We manually assign each tweet its market sentiment, i.e., the investor’s anticipation of a stock’s future return. Using this data, we show that all existing sentiment models trained on adjacent domains struggle with accurate market sentiment analysis due to the task’s specialized vocabulary. Consequently, we design, train, and deploy our own sentiment model. It outperforms all previous models (VADER, NTUSD-Fin, FinBERT, TwitterRoBERTa) when evaluated on Twitter posts. On posts from a different platform, our model performs on par with BERT-based large language models. We achieve this result at a fraction of the training and inference costs due to the model’s simple design. We publish the artifact as a python library to facilitate its use by future researchers and practitioners. KW - sentiment analysis KW - financial market sentiment KW - opinion mining KW - machine learning KW - deep learning Y1 - 2023 U6 - https://doi.org/10.1016/j.jjimei.2023.100171 SN - 2667-0968 VL - 3 IS - 1 PB - Elsevier CY - Amsterdam ER - TY - JOUR A1 - Brandes, Stefanie A1 - Sicks, Florian A1 - Berger, Anne T1 - Behaviour classification on giraffes (Giraffa camelopardalis) using machine learning algorithms on triaxial acceleration data of two commonly used GPS devices and its possible application for their management and conservation JF - Sensors N2 - Averting today's loss of biodiversity and ecosystem services can be achieved through conservation efforts, especially of keystone species. Giraffes (Giraffa camelopardalis) play an important role in sustaining Africa's ecosystems, but are 'vulnerable' according to the IUCN Red List since 2016. Monitoring an animal's behavior in the wild helps to develop and assess their conservation management. One mechanism for remote tracking of wildlife behavior is to attach accelerometers to animals to record their body movement. We tested two different commercially available high-resolution accelerometers, e-obs and Africa Wildlife Tracking (AWT), attached to the top of the heads of three captive giraffes and analyzed the accuracy of automatic behavior classifications, focused on the Random Forests algorithm. For both accelerometers, behaviors of lower variety in head and neck movements could be better predicted (i.e., feeding above eye level, mean prediction accuracy e-obs/AWT: 97.6%/99.7%; drinking: 96.7%/97.0%) than those with a higher variety of body postures (such as standing: 90.7-91.0%/75.2-76.7%; rumination: 89.6-91.6%/53.5-86.5%). Nonetheless both devices come with limitations and especially the AWT needs technological adaptations before applying it on animals in the wild. Nevertheless, looking at the prediction results, both are promising accelerometers for behavioral classification of giraffes. Therefore, these devices when applied to free-ranging animals, in combination with GPS tracking, can contribute greatly to the conservation of giraffes. KW - giraffe KW - triaxial acceleration KW - machine learning KW - random forests KW - behavior classification KW - giraffe conservation Y1 - 2021 U6 - https://doi.org/10.3390/s21062229 SN - 1424-8220 VL - 21 IS - 6 PB - MDPI CY - Basel ER - TY - JOUR A1 - Hampf, Anna A1 - Nendel, Claas A1 - Strey, Simone A1 - Strey, Robert T1 - Biotic yield losses in the Southern Amazon, Brazil BT - making use of smartphone-assisted plant disease diagnosis data JF - Frontiers in plant science : FPLS N2 - Pathogens and animal pests (P&A) are a major threat to global food security as they directly affect the quantity and quality of food. The Southern Amazon, Brazil's largest domestic region for soybean, maize and cotton production, is particularly vulnerable to the outbreak of P&A due to its (sub)tropical climate and intensive farming systems. However, little is known about the spatial distribution of P&A and the related yield losses. Machine learning approaches for the automated recognition of plant diseases can help to overcome this research gap. The main objectives of this study are to (1) evaluate the performance of Convolutional Neural Networks (ConvNets) in classifying P&A, (2) map the spatial distribution of P&A in the Southern Amazon, and (3) quantify perceived yield and economic losses for the main soybean and maize P&A. The objectives were addressed by making use of data collected with the smartphone application Plantix. The core of the app's functioning is the automated recognition of plant diseases via ConvNets. Data on expected yield losses were gathered through a short survey included in an "expert" version of the application, which was distributed among agronomists. Between 2016 and 2020, Plantix users collected approximately 78,000 georeferenced P&A images in the Southern Amazon. The study results indicate a high performance of the trained ConvNets in classifying 420 different crop-disease combinations. Spatial distribution maps and expert-based yield loss estimates indicate that maize rust, bacterial stalk rot and the fall armyworm are among the most severe maize P&A, whereas soybean is mainly affected by P&A like anthracnose, downy mildew, frogeye leaf spot, stink bugs and brown spot. Perceived soybean and maize yield losses amount to 12 and 16%, respectively, resulting in annual yield losses of approximately 3.75 million tonnes for each crop and economic losses of US$2 billion for both crops together. The high level of accuracy of the trained ConvNets, when paired with widespread use from following a citizen-science approach, results in a data source that will shed new light on yield loss estimates, e.g., for the analysis of yield gaps and the development of measures to minimise them. KW - plant pathology KW - animal pests KW - pathogens KW - machine learning KW - digital KW - image processing KW - disease diagnosis KW - crowdsourcing KW - crop losses Y1 - 2021 U6 - https://doi.org/10.3389/fpls.2021.621168 SN - 1664-462X VL - 12 PB - Frontiers Media CY - Lausanne ER - TY - JOUR A1 - Bornhorst, Julia A1 - Nustede, Eike Jannik A1 - Fudickar, Sebastian T1 - Mass Surveilance of C. elegans-Smartphone-Based DIY Microscope and Machine-Learning-Based Approach for Worm Detection JF - Sensors N2 - The nematode Caenorhabditis elegans (C. elegans) is often used as an alternative animal model due to several advantages such as morphological changes that can be seen directly under a microscope. Limitations of the model include the usage of expensive and cumbersome microscopes, and restrictions of the comprehensive use of C. elegans for toxicological trials. With the general applicability of the detection of C. elegans from microscope images via machine learning, as well as of smartphone-based microscopes, this article investigates the suitability of smartphone-based microscopy to detect C. elegans in a complete Petri dish. Thereby, the article introduces a smartphone-based microscope (including optics, lighting, and housing) for monitoring C. elegans and the corresponding classification via a trained Histogram of Oriented Gradients (HOG) feature-based Support Vector Machine for the automatic detection of C. elegans. Evaluation showed classification sensitivity of 0.90 and specificity of 0.85, and thereby confirms the general practicability of the chosen approach. KW - Caenorhabditis elegans KW - machine learning KW - smartphone KW - microscope KW - SVM KW - HOG Y1 - 2019 U6 - https://doi.org/10.3390/s19061468 SN - 1424-8220 VL - 19 IS - 6 PB - MDPI CY - Basel ER - TY - JOUR A1 - Baumgart, Lene A1 - Boos, Pauline A1 - Eckstein, Bernd T1 - Datafication and algorithmic contingency BT - how agile organisations deal with technical systems JF - Work organisation, labour & globalisation N2 - In the context of persistent images of self-perpetuated technologies, we discuss the interplay of digital technologies and organisational dynamics against the backdrop of systems theory. Building on the case of an international corporation that, during an agile reorganisation, introduced an AI-based personnel management platform, we show how technical systems produce a form of algorithmic contingency that subsequently leads to the emergence of formal and informal interaction systems. Using the concept of datafication, we explain how these interactions are barriers to the self-perpetuation of data-based decision-making, making it possible to take into consideration further decision factors and complementing the output of the platform. The research was carried out within the scope of the research project ‘Organisational Implications of Digitalisation: The Development of (Post-)Bureaucratic Organisational Structures in the Context of Digital Transformation’ funded by the German Research Foundation (DFG). KW - digitalisation KW - datafication KW - organisation KW - agile KW - technical system KW - systems theory KW - interaction KW - algorithmic contingency KW - machine learning KW - platform Y1 - 2023 U6 - https://doi.org/10.13169/workorgalaboglob.17.1.0061 SN - 1745-641X SN - 1745-6428 VL - 17 IS - 1 SP - 61 EP - 73 PB - Pluto Journals CY - London ER - TY - JOUR A1 - Kühn, Daniela A1 - Hainzl, Sebastian A1 - Dahm, Torsten A1 - Richter, Gudrun A1 - Vera Rodriguez, Ismael T1 - A review of source models to further the understanding of the seismicity of the Groningen field JF - Netherlands journal of geosciences : NJG N2 - The occurrence of felt earthquakes due to gas production in Groningen has initiated numerous studies and model attempts to understand and quantify induced seismicity in this region. The whole bandwidth of available models spans the range from fully deterministic models to purely empirical and stochastic models. In this article, we summarise the most important model approaches, describing their main achievements and limitations. In addition, we discuss remaining open questions and potential future directions of development. KW - deterministic KW - empirical KW - hybrid KW - machine learning KW - seismicity model Y1 - 2022 U6 - https://doi.org/10.1017/njg.2022.7 SN - 0016-7746 SN - 1573-9708 VL - 101 PB - Cambridge Univ. Press CY - Cambridge ER - TY - JOUR A1 - Ebers, Martin A1 - Hoch, Veronica R. S. A1 - Rosenkranz, Frank A1 - Ruschemeier, Hannah A1 - Steinrötter, Björn T1 - The European Commission’s proposal for an Artificial Intelligence Act BT - a critical assessment by members of the Robotics and AI Law Society (RAILS) JF - J : multidisciplinary scientific journal N2 - On 21 April 2021, the European Commission presented its long-awaited proposal for a Regulation “laying down harmonized rules on Artificial Intelligence”, the so-called “Artificial Intelligence Act” (AIA). This article takes a critical look at the proposed regulation. After an introduction (1), the paper analyzes the unclear preemptive effect of the AIA and EU competences (2), the scope of application (3), the prohibited uses of Artificial Intelligence (AI) (4), the provisions on high-risk AI systems (5), the obligations of providers and users (6), the requirements for AI systems with limited risks (7), the enforcement system (8), the relationship of the AIA with the existing legal framework (9), and the regulatory gaps (10). The last section draws some final conclusions (11). KW - artificial intelligence KW - machine learning KW - European Union KW - regulation KW - harmonization KW - Artificial Intelligence Act Y1 - 2021 U6 - https://doi.org/10.3390/j4040043 SN - 2571-8800 VL - 4 IS - 4 SP - 589 EP - 603 PB - MDPI CY - Basel ER -