@article{AdnanSrsicVenticichetal.2020, author = {Adnan, Hassan Sami and Srsic, Amanda and Venticich, Pete Milos and Townend, David M.R.}, title = {Using AI for mental health analysis and prediction in school surveys}, series = {European journal of public health}, volume = {30}, journal = {European journal of public health}, publisher = {Oxford Univ. Press}, address = {Oxford [u.a.]}, issn = {1101-1262}, doi = {10.1093/eurpub/ckaa165.336}, pages = {V125 -- V125}, year = {2020}, abstract = {Background: Childhood and adolescence are critical stages of life for mental health and well-being. Schools are a key setting for mental health promotion and illness prevention. One in five children and adolescents have a mental disorder, about half of mental disorders beginning before the age of 14. Beneficial and explainable artificial intelligence can replace current paper- based and online approaches to school mental health surveys. This can enhance data acquisition, interoperability, data driven analysis, trust and compliance. This paper presents a model for using chatbots for non-obtrusive data collection and supervised machine learning models for data analysis; and discusses ethical considerations pertaining to the use of these models. Methods: For data acquisition, the proposed model uses chatbots which interact with students. The conversation log acts as the source of raw data for the machine learning. Pre-processing of the data is automated by filtering for keywords and phrases. Existing survey results, obtained through current paper-based data collection methods, are evaluated by domain experts (health professionals). These can be used to create a test dataset to validate the machine learning models. Supervised learning can then be deployed to classify specific behaviour and mental health patterns. Results: We present a model that can be used to improve upon current paper-based data collection and manual data analysis methods. An open-source GitHub repository contains necessary tools and components of this model. Privacy is respected through rigorous observance of confidentiality and data protection requirements. Critical reflection on these ethics and law aspects is included in the project. Conclusions: This model strengthens mental health surveillance in schools. The same tools and components could be applied to other public health data. Future extensions of this model could also incorporate unsupervised learning to find clusters and patterns of unknown effects.}, language = {en} } @article{AndresBruttelFriedrichsen2022, author = {Andres, Maximilian and Bruttel, Lisa and Friedrichsen, Jana}, title = {How communication makes the difference between a cartel and tacit collusion}, series = {European economic review}, volume = {152}, journal = {European economic review}, publisher = {Elsevier}, address = {Amsterdam}, issn = {0014-2921}, doi = {10.1016/j.euroecorev.2022.104331}, pages = {1 -- 18}, year = {2022}, abstract = {This paper sheds new light on the role of communication for cartel formation. Using machine learning to evaluate free-form chat communication among firms in a laboratory experiment, we identify typical communication patterns for both explicit cartel formation and indirect attempts to collude tacitly. We document that firms are less likely to communicate explicitly about price fixing and more likely to use indirect messages when sanctioning institutions are present. This effect of sanctions on communication reinforces the direct cartel-deterring effect of sanctions as collusion is more difficult to reach and sustain without an explicit agreement. Indirect messages have no, or even a negative, effect on prices.}, language = {en} } @article{Ayzel2021, author = {Ayzel, Georgy}, title = {Deep neural networks in hydrology}, series = {Vestnik of Saint Petersburg University. Earth Sciences}, volume = {66}, journal = {Vestnik of Saint Petersburg University. Earth Sciences}, number = {1}, publisher = {Univ. Press}, address = {St. Petersburg}, issn = {2541-9668}, doi = {10.21638/spbu07.2021.101}, pages = {5 -- 18}, year = {2021}, abstract = {For around a decade, deep learning - the sub-field of machine learning that refers to artificial neural networks comprised of many computational layers - modifies the landscape of statistical model development in many research areas, such as image classification, machine translation, and speech recognition. Geoscientific disciplines in general and the field of hydrology in particular, also do not stand aside from this movement. Recently, the proliferation of modern deep learning-based techniques and methods has been actively gaining popularity for solving a wide range of hydrological problems: modeling and forecasting of river runoff, hydrological model parameters regionalization, assessment of available water resources. identification of the main drivers of the recent change in water balance components. This growing popularity of deep neural networks is primarily due to their high universality and efficiency. The presented qualities, together with the rapidly growing amount of accumulated environmental information, as well as increasing availability of computing facilities and resources, allow us to speak about deep neural networks as a new generation of mathematical models designed to, if not to replace existing solutions, but significantly enrich the field of geophysical processes modeling. This paper provides a brief overview of the current state of the field of development and application of deep neural networks in hydrology. Also in the following study, the qualitative long-term forecast regarding the development of deep learning technology for managing the corresponding hydrological modeling challenges is provided based on the use of "Gartner Hype Curve", which in the general details describes a life cycle of modern technologies.}, language = {en} } @article{AyzelIzhitskiy2019, author = {Ayzel, Georgy and Izhitskiy, Alexander}, title = {Climate Change Impact Assessment on Freshwater Inflow into the Small Aral Sea}, series = {Water}, volume = {11}, journal = {Water}, number = {11}, publisher = {MDPI}, address = {Basel}, issn = {2073-4441}, doi = {10.3390/w11112377}, pages = {19}, year = {2019}, abstract = {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.}, language = {en} } @article{BaumgartBoosEckstein2023, author = {Baumgart, Lene and Boos, Pauline and Eckstein, Bernd}, title = {Datafication and algorithmic contingency}, series = {Work organisation, labour \& globalisation}, volume = {17}, journal = {Work organisation, labour \& globalisation}, number = {1}, publisher = {Pluto Journals}, address = {London}, issn = {1745-641X}, doi = {10.13169/workorgalaboglob.17.1.0061}, pages = {61 -- 73}, year = {2023}, abstract = {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).}, language = {en} } @article{BornhorstNustedeFudickar2019, author = {Bornhorst, Julia and Nustede, Eike Jannik and Fudickar, Sebastian}, title = {Mass Surveilance of C. elegans-Smartphone-Based DIY Microscope and Machine-Learning-Based Approach for Worm Detection}, series = {Sensors}, volume = {19}, journal = {Sensors}, number = {6}, publisher = {MDPI}, address = {Basel}, issn = {1424-8220}, doi = {10.3390/s19061468}, pages = {14}, year = {2019}, abstract = {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.}, language = {en} } @article{BrandesSicksBerger2021, author = {Brandes, Stefanie and Sicks, Florian and Berger, Anne}, title = {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}, series = {Sensors}, volume = {21}, journal = {Sensors}, number = {6}, publisher = {MDPI}, address = {Basel}, issn = {1424-8220}, doi = {10.3390/s21062229}, pages = {22}, year = {2021}, abstract = {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.}, language = {en} } @article{CeulemansGuillGaedke2021, author = {Ceulemans, Ruben and Guill, Christian and Gaedke, Ursula}, title = {Top predators govern multitrophic diversity effects in tritrophic food webs}, series = {Ecology : a publication of the Ecological Society of America}, volume = {102}, journal = {Ecology : a publication of the Ecological Society of America}, number = {7}, publisher = {Wiley}, address = {Hoboken}, issn = {0012-9658}, doi = {10.1002/ecy.3379}, pages = {16}, year = {2021}, abstract = {It is well known that functional diversity strongly affects ecosystem functioning. However, even in rather simple model communities consisting of only two or, at best, three trophic levels, the relationship between multitrophic functional diversity and ecosystem functioning appears difficult to generalize, because of its high contextuality. In this study, we considered several differently structured tritrophic food webs, in which the amount of functional diversity was varied independently on each trophic level. To achieve generalizable results, largely independent of parametrization, we examined the outcomes of 128,000 parameter combinations sampled from ecologically plausible intervals, with each tested for 200 randomly sampled initial conditions. Analysis of our data was done by training a random forest model. This method enables the identification of complex patterns in the data through partial dependence graphs, and the comparison of the relative influence of model parameters, including the degree of diversity, on food-web properties. We found that bottom-up and top-down effects cascade simultaneously throughout the food web, intimately linking the effects of functional diversity of any trophic level to the amount of diversity of other trophic levels, which may explain the difficulty in unifying results from previous studies. Strikingly, only with high diversity throughout the whole food web, different interactions synergize to ensure efficient exploitation of the available nutrients and efficient biomass transfer to higher trophic levels, ultimately leading to a high biomass and production on the top level. The temporal variation of biomass showed a more complex pattern with increasing multitrophic diversity: while the system initially became less variable, eventually the temporal variation rose again because of the increasingly complex dynamical patterns. Importantly, top predator diversity and food-web parameters affecting the top trophic level were of highest importance to determine the biomass and temporal variability of any trophic level. Overall, our study reveals that the mechanisms by which diversity influences ecosystem functioning are affected by every part of the food web, hampering the extrapolation of insights from simple monotrophic or bitrophic systems to complex natural food webs.}, language = {en} } @article{ChenLangeAndjelkovicetal.2022, author = {Chen, Junchao and Lange, Thomas and Andjelkovic, Marko and Simevski, Aleksandar and Lu, Li and Krstić, Miloš}, title = {Solar particle event and single event upset prediction from SRAM-based monitor and supervised machine learning}, series = {IEEE transactions on emerging topics in computing / IEEE Computer Society, Institute of Electrical and Electronics Engineers}, volume = {10}, journal = {IEEE transactions on emerging topics in computing / IEEE Computer Society, Institute of Electrical and Electronics Engineers}, number = {2}, publisher = {Institute of Electrical and Electronics Engineers}, address = {[New York, NY]}, issn = {2168-6750}, doi = {10.1109/TETC.2022.3147376}, pages = {564 -- 580}, year = {2022}, abstract = {The intensity of cosmic radiation may differ over five orders of magnitude within a few hours or days during the Solar Particle Events (SPEs), thus increasing for several orders of magnitude the probability of Single Event Upsets (SEUs) in space-borne electronic systems. Therefore, it is vital to enable the early detection of the SEU rate changes in order to ensure timely activation of dynamic radiation hardening measures. In this paper, an embedded approach for the prediction of SPEs and SRAM SEU rate is presented. The proposed solution combines the real-time SRAM-based SEU monitor, the offline-trained machine learning model and online learning algorithm for the prediction. With respect to the state-of-the-art, our solution brings the following benefits: (1) Use of existing on-chip data storage SRAM as a particle detector, thus minimizing the hardware and power overhead, (2) Prediction of SRAM SEU rate one hour in advance, with the fine-grained hourly tracking of SEU variations during SPEs as well as under normal conditions, (3) Online optimization of the prediction model for enhancing the prediction accuracy during run-time, (4) Negligible cost of hardware accelerator design for the implementation of selected machine learning model and online learning algorithm. The proposed design is intended for a highly dependable and self-adaptive multiprocessing system employed in space applications, allowing to trigger the radiation mitigation mechanisms before the onset of high radiation levels.}, language = {en} } @article{CopeBaukmannKlingeretal.2021, author = {Cope, Justin L. and Baukmann, Hannes A. and Klinger, J{\"o}rn E. and Ravarani, Charles N. J. and B{\"o}ttinger, Erwin and Konigorski, Stefan and Schmidt, Marco F.}, title = {Interaction-based feature selection algorithm outperforms polygenic risk score in predicting Parkinson's Disease status}, series = {Frontiers in genetics}, volume = {12}, journal = {Frontiers in genetics}, publisher = {Frontiers Media}, address = {Lausanne}, issn = {1664-8021}, doi = {10.3389/fgene.2021.744557}, pages = {9}, year = {2021}, abstract = {Polygenic risk scores (PRS) aggregating results from genome-wide association studies are the state of the art in the prediction of susceptibility to complex traits or diseases, yet their predictive performance is limited for various reasons, not least of which is their failure to incorporate the effects of gene-gene interactions. Novel machine learning algorithms that use large amounts of data promise to find gene-gene interactions in order to build models with better predictive performance than PRS. Here, we present a data preprocessing step by using data-mining of contextual information to reduce the number of features, enabling machine learning algorithms to identify gene-gene interactions. We applied our approach to the Parkinson's Progression Markers Initiative (PPMI) dataset, an observational clinical study of 471 genotyped subjects (368 cases and 152 controls). With an AUC of 0.85 (95\% CI = [0.72; 0.96]), the interaction-based prediction model outperforms the PRS (AUC of 0.58 (95\% CI = [0.42; 0.81])). Furthermore, feature importance analysis of the model provided insights into the mechanism of Parkinson's disease. For instance, the model revealed an interaction of previously described drug target candidate genes TMEM175 and GAPDHP25. These results demonstrate that interaction-based machine learning models can improve genetic prediction models and might provide an answer to the missing heritability problem.}, language = {en} }