Florent Thouvenin, Jacques de Werra, Yaniv Benhamou, Abraham Bernstein, Felix Gille, Diego Kuonen, Christian Lovis, Stephanie Volz, Viktor von Wyl, Governance Mechanisms for Access and Use of Data in Public Health Crises: Call for Action, Jusletter (1128), 2022. (Journal Article)
 
The Covid 19 pandemic demonstrated the importance of access to data to en- sure that authorities can make informed decisions in the event of a crisis. How- ever, there are currently three types of barriers preventing access and use of data: (i) technical barriers, especially the lack of uniform data formats and semantics; (ii) legal barriers, especially data protection that limits the use of personal data; and (iii) societal barriers, especially the lack of data literacy and trust. This call for action presents ways to overcome these barriers and pro- poses new governance mechanisms for the access and use of data in public health crises. |
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Ombretta Strafforello, Vanathi Rajasekart, Osman S Kayhan, Oana Inel, Jan van Gemert, Humans disagree with the IoU for measuring object detector localization error, In: 2022 IEEE International Conference on Image Processing (ICIP), -, 2022. (Conference or Workshop Paper published in Proceedings)

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Yasara Peiris, Clara-Maria Barth, Elaine May Huang, Jürgen Bernard, A Data-Centric Methodology and Task Typology for Time-Stamped Event Sequences, In: VIS Workshop on Evaluation and Beyond -- Methodological Approaches for Visualization (BELIV), IEEE, 2022. (Conference or Workshop Paper published in Proceedings)
 
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Koteru Ramani Navya, Yash Patel, Parminder Kaur Makode, Blockchain Application in Incentivizing Students on Participation in Online Classes, In: 2022 IEEE 2nd Mysore Sub Section International Conference (MysuruCon), IEEE, 2022. (Conference or Workshop Paper published in Proceedings)

The world we know has changed over a brief
period, with the ascent and spread of Covid-19. This affected the
education sector resulting in offline classes to online classes.
Technologies made it easy with numerous websites, materials,
video lectures, courses, and techniques for the students. In this
situation, the main problem arises with students not
participating in the class having many reasons such as illness,
being introverted, and feeling that they may be wrong. If we are
not interested in something to talk about or are shy, we must
face it so that we will make the best out of it. People remember
the things they listen to carefully, so we can probably study less
if we listen. In this research paper, we proposed a Blockchain
based application, so students who are going to attend online
classes would be able to participate more in the class. They will
be able to gain incentives that are based on crypto currency and
by using those cryptocurrencies they can spend it on fees or any
other resources in the university which would be beneficial for
students. They will be able to gain incentives that are based on
cryptocurrency and by using those crypto currencies they can
spend it on fees or any other resources in the university. We are
using the most popular technology which is Blockchain technology to make sure that students who are attending online classes will be able to pay more attention. Also, we are using the most famous functionality of Blockchain which is
incentivization. To give rewards to the students we will be using incentivization so they can pay more attention to the classes. This design is beneficial for teachers also to look at the status of each student and get in contact with them. |
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Eveline Kobler, Thorsten Hens, Wie lebt es sich mit Inflation?, In: Schweizer Radio und Fernsehen SRF, 16 October 2022. (Media Coverage)

Nach einer langen Phase mit praktisch keiner Teuerung steuert die Welt zurzeit auf eine längere Zeit zu, in der Preise kontinuierlich steigen. Gerade die jüngere Generation kennt das noch gar nicht, wie es ist, wenn das Leben laufend teurer wird. Wie lebt es sich also in so einer Phase? Stellungnahmen von Rudolf Strahm (Ökonom), Thorsten Hens (Uni Zürich), Anastassios Frangulidis (Pictet). |
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Wenjie Jia, Linyuan Lu, Manuel Mariani, Yueyue Dai, Tao Jiang, Towards detecting previously undiscovered interaction types in networked systems, IEEE Internet of Things Journal, Vol. 9 (20), 2022. (Journal Article)
 
Studying networked systems in a variety of domains, including biology, social science and internet of things, has recently received a surge of attention. For a networked system, there are usually multiple types of interactions between its components, and such interaction type information is crucial since it always associated with important features. However, some interaction types which actually exist in the network may not be observed in the metadata collected in practice. This paper proposes an approach aiming to detect previously undiscovered interaction types (PUITs) in networked systems. The first step in our proposed PUIT detection approach is to answer the following fundamental question: is it possible to effectively detect PUITs without utilizing metadata other than the existing incomplete interaction type information and the connection information of the system? Here, we first propose a temporal network model which can be used to mimic any real network and then discover that some special networks which fit the model shall a common topological property. Supported by this discovery, we finally develop a PUIT detection method for networks which fit the proposed model. Both analytical and numerical results show this detection method is more effective than the baseline method, demonstrating that effectively detecting PUITs in networks is achievable. More studies on PUIT detection are of significance and in great need since this approach should be as essential as the previously undiscovered node type detection which has gained great success in the field of biology. |
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Marc Chesney, CS: la débâcle de Casino Suisse, In: Le Temps, p. online, 14 October 2022. (Newspaper Article)

Il n’est pas acceptable que pouvoirs publics comme analystes financiers aient fermé les yeux si longtemps sur les pertes à répétition de Credit Suisse (qui n’ont pas empêché de fortes rémunérations) car au final ce sont les contribuables qui paieraient la casse le cas échéant, avertit le professeur d’économie. |
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Daniel Schunk, Eva M Berger, Henning Hermes, Kirsten Winkel, Ernst Fehr, Teaching self-regulation, Nature Human Behaviour, Vol. 6 (12), 2022. (Journal Article)
 
Children’s self-regulation abilities are key predictors of educational success and other life outcomes such as income and health. However, self-regulation is not a school subject, and knowledge about how to generate lasting improvements in self-regulation and academic achievements with easily scalable, low-cost interventions is still limited. Here we report the results of a randomized controlled field study that integrates a short self-regulation teaching unit based on the concept of mental contrasting with implementation intentions into the school curriculum of first graders. We demonstrate that the treatment increases children’s skills in terms of impulse control and self-regulation while also generating lasting improvements in academic skills such as reading and monitoring careless mistakes. Moreover, it has a substantial effect on children’s long-term school career by increasing the likelihood of enroling in an advanced secondary school track three years later. Thus, self-regulation teaching can be integrated into the regular school curriculum at low cost, is easily scalable, and can substantially improve important abilities and children’s educational career path. |
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Ibrahim Al-Hazwani, Gabriela Morgenshtern, Yves Rutishauser, Mennatallah El-Assady, Jürgen Bernard, What Shall We Watch Tonight?: Why sometimes your favourite streaming service just cannot manage to recommend anything interesting, In: IEEE VIS Workshop on Visualization for AI Explainability, IEEE, 2022. (Conference or Workshop Paper published in Proceedings)
 
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Klaus Ammann, Alexander Wagner, Nachhaltig investieren: Klimasünder beeinflussen oder abstossen? , In: SRF, 10 October 2022. (Media Coverage)

Kompletter Ausstieg aus klimaschädlichen Anlagen oder die Unternehmen positiv beeinflussen? Die Meinungen sind geteilt. |
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Redaktion, Alexander Wagner, Sustainable Finance vor der Bewährungsprobe in einer sich wandelnden Weltordnung, In: The Onliner, 7 October 2022. (Media Coverage)
 
Bis vor Kurzem war Sustainable Finance eine umfassende Erfolgsgeschichte. Während die Wirtschaft und die
Finanzmärkte prosperierten und die Unternehmen über reichlich Liquidität verfügten, boomten auch die
nachhaltigen Investitionen. Selbst der brutale, aber im Grossen und Ganzen kurzlebige Schock der Covid-19-
Pandemie konnte diese Entwicklung nicht aufhalten. |
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Fredy Greuter, Alexander Wagner, Ist nachhaltiges Anlegen eine «kaputte Idee»?, In: finews.ch, 7 October 2022. (Media Coverage)
 
Die Unsummen von Geldern, die in nachhaltige Anlagen gesteckt werden, sind zwar kein unwirksames Placebo. In diesen Krisenmonaten muss diese Anlageform allerdings den ersten echten Härtetest bestehen, wie sich an einer Konferenz in Zürich herausstellte. |
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Stefan Zeisberger, Do People Care about Loss Probabilities?, Journal of Risk and Uncertainty, Vol. 65, 2022. (Journal Article)
 
In a series of experiments, we provide evidence that people pay special attention to the probability of losing. We first analyze this behavior in the typically used one-shot choice tasks. We then extend our analysis to repeated decisions in choice tasks, as well as allocation and investment tasks. Additionally, we test both decision making under risk and under gradually removed uncertainty, as with decisions from experience. Our findings of explicit attention to loss probabilities contradict the predictions of normative and descriptive decision theories, such as Expected Utility Theory and (Cumulative) Prospect Theory. We suggest a value function with a jump rather than a kink at the reference point, which separates gains and losses. |
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Hsuan-Pin Wu, Alternative Algorithmic Approaches for Crew Diagramming, University of Zurich, Faculty of Business, Economics and Informatics, 2022. (Master's Thesis)
 
The crew diagramming problem has been a challenging task in the railway industry. The massive amount of tasks, requiring multiple crew members to work together under a large set of constrains, create a large scale optimization problem. The common approach to solve this problem which is widely used by the railway companies, is to formulate an integer linear program and decompose it by column generation into a master problem and a sub-problem. However, the sub-problem is often a resource constrained shortest path problem which is NP-hard and the computational time is in the order of hours to days.

We designed an alternative algorithmic approach for the crew diagramming by performing Danztig-Wolfe decomposition on a flow based linear program with set partition constraint. Even though the master problem is still a set partition LP with column generation and minimum cost flow pricing problem, we reduced the number of constraints and avoided solving an NP-hard problem. In order to speed up solving the pricing problem, we split the original network into multiple sub-networks and paralellized the computation of the pricing models. In the end we solved the master problem as an integer programming problem only with the variables selected by the column generation.

This optimization approach was then implemented on real world data provided by Swiss Railways. We compared the results in terms of computational time with the flow based optimizer developed by Algomia GmbH and found that for small and medium-sized depots, our approach is faster on average when compared to the computation time required to arrive only at the LP-solution with Algomia's algorithm. When comparing our results in terms of the number of diagrams with those produced by manual planners, we found a consistent improvement for larger-size depots, where slight adjustments to the constraints often performed by planners have less of an impact on the overall result. |
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Niels Kübler, Design and Implementation of a Verifiable Remote Postal Voting System, University of Zurich, Faculty of Business, Economics and Informatics, 2022. (Master's Thesis)
 
Switzerland allows its citizens to participate in elections and votes by casting ballots via postal mail. This practice, referred to as “Remote Postal Voting”, is a convenient way for voters to cast their ballots without visiting a polling station. However, the application of Remote Postal Voting has raised security concerns regarding vote- and election manipulations by voting officials or third parties. These concerns have been confirmed in practice, where malicious activities have been discovered on multiple occasions. This thesis aims at providing verifiability for Remote Postal Voting procedures by presenting the design and implementation of a verifiable Remote Postal Voting system. The design integrates well into the existing Swiss Remote Postal Voting procedures. It leverages blockchain technology, Homomorphic Encryption, Non-Interactive Zero Knowledge Proofs, and Threshold Cryptography to meet privacy and verifiability requirements. The evaluation shows that the proposed system scales well in the Swiss Remote Postal Voting scenario, whereas further optimization would be necessary for larger countries. |
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Weijie Niu, Prediction of Paroxysmal Atrial Fibrillation Enabled by Machine Learning, University of Zurich, Faculty of Business, Economics and Informatics, 2022. (Master's Thesis)
 
Atrial fibrillation (AF) is the most common type of arrhythmia occurring as an irregular excitation from the ventricles that affects the function of the heart and increases the risk of stroke and heart attack, leading to an extremely high mortality rate worldwide. Nearly half of all patients with AF are those with paroxysmal atrial fibrillation (PAF), and the chances of cure with medical intervention at this stage are very high compared to later stages. Therefore, the early detection and prediction of PAF are clinically important. However, due to the asymptomatic and interim episodic nature of PAF, its early detection and onset prediction have been challenging topics. Recent advances in the field of artificial intelligence, particularly machine learning techniques based on electrocardiogram(ECG) data, including deep learning, have enabled the development of PAF prediction. The goal of this work is to review the published studies related to PAF prediction, and predict the onset of PAF in advance with the shortened ECG signals and a high degree of accuracy. We systematically review the publications over the past 10 years, focusing on the prediction of PAF enabled by ECG-based machine learning models. Totally 15 studies of both traditional machine learning and deep learning models proposed over the past decade are covered and reviewed in this work. We propose a novel neural network framework PAFNet based on a convolution neural networks (CNN) with residual structure, a bi-directional Long-short-term memory network (LSTM) and the attention mechanism to predict the onset of PAF in advance. The PAFNet model is evaluated on public datasets and compared with previous studies with good performance. We describe the ideas behind the model design and analyze and discuss the results of the experiments. Finally we also discuss the potential future progress and challenges in the field. |
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Joël Rüttimann, Graffiti Stencils: Automated creation of graffiti stencils, University of Zurich, Faculty of Business, Economics and Informatics, 2022. (Bachelor's Thesis)
 
Graffiti are omnipresent in today’s urban areas. A special form of graffiti are stencils which are mostly two-tone and easy to replicate. The research objective of the present thesis is to evaluate whether it is possible to automatically create stencils from an arbitrary image. While research on image abstraction is present in relevant literature, automatic
stencil creation is understudied in scholarly work. In order to automatically create a stencil, a known algorithm is implemented and extended to create artistically pleasing stencils. The created stencils are then assessed using guidelines driven by research and expert input. Consequently, this work shows that the combination of already existing algorithms with carefully chosen parameters leads to the production of objectively wellmade
stencils. |
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Krzysztof Wroblewski, Using Artificial Intelligence for the Reduction of Emissions in Cities – Creating sustainable transportation with AI, University of Zurich, Faculty of Business, Economics and Informatics, 2022. (Bachelor's Thesis)
 
This bachelor thesis investigates different use cases of using artificial intelligence to reduce city transport emissions with a focus on using machine learning to forecast passenger demand. We first conducted a literature review to gather the different uses of artificial intelligence in city transport emission reduction. Then we developed a prototype that uses artificial intelligence to predict the public transport passenger demand for the city of Zurich using the design science research method. Last we presented and evaluated the usability of the prototype in a workshop.
Our results highlight applications of artificial intelligence that can be used in city transport emission reduction. Our prototype shows how artificial intelligence can be used to predict public transport passenger demand in the city of Zurich. The findings from the evaluation show how transport decision makers can use our prototype to reduce city transport emissions,
We conclude that artificial intelligence methods can be used to support measures that reduce transport emissions in cities, by providing citiesí decision makers with information about traffic, passenger demand, mobility patterns, transport network design, electrification, and emissions. Passenger demand predictions made by artificial intelligence can be used by transport companies to reduce transport emissions through better public transport planning. |
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Yingying Chen, Explainable Classification of COVID-19 in Chest X-ray Images, University of Zurich, Faculty of Business, Economics and Informatics, 2022. (Master's Thesis)
 
Nowadays, developing an automated diagnosis system that can detect COVID-19 or other pneumonia from CXR images without human intervention still meets with great challenges when the system lacks interpretability of the deep learning model. We need not only the high accuracy of the model but also the interpretability of the model. In this work, we focus on the explainable classification of chest X-ray images. We pay attention not only to COVID-19 but other kinds of lung infections. First, we propose three new and simple visualization methods to improve the interpretability of the deep learning model. The proposed methods are based on the class activation map (CAM) framework. Second, we propose a quantitative metric, acceptable mask ratio, to evaluate the interpretability of the deep learning model so that we can assess different methods intuitively.
Through experiments, we find that better performance of the model does not necessarily correspond to better interpretability. With the help of acceptable masking rates, we can contribute to a certain extent to the selection of models with high accuracy and good interpretability for automated diagnostic systems. Furthermore, the proposed visualization methods can be used to interpret the classification results of deep learning models and help clinicians to build more credible diagnostic models. |
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Andy Agudelo Londono, Cause-related Marketing vs. CSR: Eine Online-Umfrage zur Wahrnehmung nachhaltiger Mode, University of Zurich, Faculty of Business, Economics and Informatics, 2022. (Bachelor's Thesis)

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