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The present research in high energy physics as well as in the nuclear physics requires the use of more powerful and complex particle accelerators to provide high luminosity, high intensity, and high brightness beams to experiments. With the increased technological complexity of accelerators, meeting the demand of experimenters necessitates a blend of accelerator physics with technology. The problem becomes severe when optimization of beam quality has to be provided in accelerator systems with thousands of free parameters including strengths of quadrupoles, sextupoles, RF voltages, etc. Machine learning methods and concepts of artificial intelligence are considered in various industry and scientific branches, and recently, these methods are used in high energy physics mainly for experiments data analysis.
In Accelerator Physics the machine learning approach has not found a wide application yet, and in general the use of these methods is carried out without a deep understanding on their effectiveness with respect to more traditional schemes or other alternative approaches. The purpose of this PhD research is to investigate the methods of machine learning applied to accelerator optimization, accelerator control and in particular on optics measurements and corrections. Optics correction, maximization of acceptance, and simultaneous control of various accelerator components such as focusing magnets is a typical accelerator scenario. The effectiven- ess of machine learning methods in a complex system such as the Large Hadron Collider, which beam dynamics exhibits nonlinear response to machine settings is the core of the study. This work presents successful application of several machine learning techniques such as clustering, decision trees, linear multivariate models and neural networks to beam optics measurements and corrections at the LHC, providing the guidelines for incorporation of machine learning techniques into accelerator operation and discussing future opportunities and potential work in this field.
Reproducible annotations
(2022)
This bachelor thesis presents a software solution which implements reproducible annotations in the context of the UIMA framework. This is achieved by creating an automated containerization of arbitrary analysis engines and annotating every analysis engine configuration in the processed CAS document. Any CAS document created by this solution is self sufficient and able to reproduce the exact environment under which it was created.
A review of the state-of-the art software in the field of UIMA reveals that there are many implementations trying to increase reproducibility for a given application relying on UIMA, but no publication trying to increase the reproducibility of UIMA itself. This thesis improves upon that technological gap and provides a throughout analysis at the end which shows a negligible overhead in memory consumption, but a significant performance regression depending on the complexity of the analysis engine which was examined.
Ein aktuelles Forschungsthema ist die automatische Generierung von 3D-Szenen ausgehend von Beschreibungen in natürlicher Sprache. S.g. Text2Scene-Anwendungen sollen Objekte und räumliche Relationen in einer Texteingabe identifizieren und mit 3D-Modellen eine visuelle Repräsentation der Beschreibung konstruieren. Bisherige Ansätze kombinieren eine
stichwortbasierte Erkennung von explizit gemachten Angaben mit vorher gelerntem Allgemeinwissen über die sinnvolle Anordnung von Objekten. Den Anwendungen fehlt jedoch ein tiefergehendes Verständnis von räumlicher Sprache.
Mit dem Annotationsschema ISOSpace können Texte mit detaillierten räumlichen Informationen angereichert und so für NLP-Anwendungen verständlicher gemacht werden. Bereits in einer früheren Arbeit wurde der SemAF-Annotator zum Erstellen von ISOSpaceAnnotationen als Modul für den TextAnnotator entwickelt. In dieser Arbeit wurde der SemAF-Annotator zusätzlich um eine Funktionalität zur Szenenerstellung erweitert: Benutzer können einzelnen Wörtern in der Weboberfläche des TextAnnotators Objekte aus dem ShapeNet Datensatz zuordnen und diese in einer zweidimensionalen Darstellung einer Szene räumlich anordnen. Trotz einiger Einschränkungen durch die fehlende dritte Dimension lassen sich in vielen Fällen gute Ergebnisse erzielen. Die auf diese Weise erzeugten Szenen sollen später in Kombination mit den ISOSpace-Annotionen verwendet werden, um Text2SceneAnwendungen zu entwickeln, die ein umfassenderes räumliches Verständnis aufweisen.
Kleinere Nebenaufgaben dieser Arbeit waren die Erweiterung des SemAF-Annotators um zusätzliche Annotationstypen sowie diverse Nachbesserungen der bereits bestehenden Funktionalität zur ISOSpace Annotation.
The recognition of pharmacological substances, compounds and proteins is an essential preliminary work for the recognition of relations between chemicals and other biomedically relevant units. In this paper, we describe an approach to Task 1 of the PharmaCoNER Challenge, which involves the recognition of mentions of chemicals and drugs in Spanish medical texts. We train a state-of-the-art BiLSTM-CRF sequence tagger with stacked Pooled Contextualized Embeddings, word and sub-word embeddings using the open-source framework FLAIR. We present a new corpus composed of articles and papers from Spanish health science journals, termed the Spanish Health Corpus, and use it to train domain-specific embeddings which we incorporate in our model training. We achieve a result of 89.76% F1-score using pre-trained embeddings and are able to improve these results to 90.52% F1-score using specialized embeddings.
Despite the great importance of the Latin language in the past, there are relatively few resources available today to develop modern NLP tools for this language. Therefore, the EvaLatin Shared Task for Lemmatization and Part-of-Speech (POS) tagging was published in the LT4HALA workshop. In our work, we dealt with the second EvaLatin task, that is, POS tagging. Since most of the available Latin word embeddings were trained on either few or inaccurate data, we trained several embeddings on better data in the first step. Based on these embeddings, we trained several state-of-the-art taggers and used them as input for an ensemble classifier called LSTMVoter. We were able to achieve the best results for both the cross-genre and the cross-time task (90.64% and 87.00%) without using additional annotated data (closed modality). In the meantime, we further improved the system and achieved even better results (96.91% on classical, 90.87% on cross-genre and 87.35% on cross-time).
Biodiversity information is contained in countless digitized and unprocessed scholarly texts. Although automated extraction of these data has been gaining momentum for years, there are still innumerable text sources that are poorly accessible and require a more advanced range of methods to extract relevant information. To improve the access to semantic biodiversity information, we have launched the BIOfid project (www.biofid.de) and have developed a portal to access the semantics of German language biodiversity texts, mainly from the 19th and 20th century. However, to make such a portal work, a couple of methods had to be developed or adapted first. In particular, text-technological information extraction methods were needed, which extract the required information from the texts. Such methods draw on machine learning techniques, which in turn are trained by learning data. To this end, among others, we gathered the BIOfid text corpus, which is a cooperatively built resource, developed by biologists, text technologists, and linguists. A special feature of BIOfid is its multiple annotation approach, which takes into account both general and biology-specific classifications, and by this means goes beyond previous, typically taxon- or ontology-driven proper name detection. We describe the design decisions and the genuine Annotation Hub Framework underlying the BIOfid annotations and present agreement results. The tools used to create the annotations are introduced, and the use of the data in the semantic portal is described. Finally, some general lessons, in particular with multiple annotation projects, are drawn.
Are nearby places (e.g., cities) described by related words? In this article, we transfer this research question in the field of lexical encoding of geographic information onto the level of intertextuality. To this end, we explore Volunteered Geographic Information (VGI) to model texts addressing places at the level of cities or regions with the help of so-called topic networks. This is done to examine how language encodes and networks geographic information on the aboutness level of texts. Our hypothesis is that the networked thematizations of places are similar, regardless of their distances and the underlying communities of authors. To investigate this, we introduce Multiplex Topic Networks (MTN), which we automatically derive from Linguistic Multilayer Networks (LMN) as a novel model, especially of thematic networking in text corpora. Our study shows a Zipfian organization of the thematic universe in which geographical places (especially cities) are located in online communication. We interpret this finding in the context of cognitive maps, a notion which we extend by so-called thematic maps. According to our interpretation of this finding, the organization of thematic maps as part of cognitive maps results from a tendency of authors to generate shareable content that ensures the continued existence of the underlying media. We test our hypothesis by example of special wikis and extracts of Wikipedia. In this way, we come to the conclusion that geographical places, whether close to each other or not, are located in neighboring semantic places that span similar subnetworks in the topic universe.
The annotation of texts and other material in the field of digital humanities and Natural Language Processing (NLP) is a common task of research projects. At the same time, the annotation of corpora is certainly the most time- and cost-intensive component in research projects and often requires a high level of expertise according to the research interest. However, for the annotation of texts, a wide range of tools is available, both for automatic and manual annotation. Since the automatic pre-processing methods are not error-free and there is an increasing demand for the generation of training data, also with regard to machine learning, suitable annotation tools are required. This paper defines criteria of flexibility and efficiency of complex annotations for the assessment of existing annotation tools. To extend this list of tools, the paper describes TextAnnotator, a browser-based, multi-annotation system, which has been developed to perform platform-independent multimodal annotations and annotate complex textual structures. The paper illustrates the current state of development of TextAnnotator and demonstrates its ability to evaluate annotation quality (inter-annotator agreement) at runtime. In addition, it will be shown how annotations of different users can be performed simultaneously and collaboratively on the same document from different platforms using UIMA as the basis for annotation.
Wir betrachten Algorithmen für strategische Kommunikation mit Commitment Power zwischen zwei rationalen Parteien mit eigenen Interessen. Wenn eine Partei Commitment Power hat, so legt sie sich auf eine Handlungsstrategie fest und veröffentlicht diese und kann nicht mehr davon abweichen.
Beide Parteien haben Grundinformation über den Zustand der Welt. Die erste Partei (S) hat die Möglichkeit, diesen direkt zu beobachten. Die zweite Partei (R) trifft jedoch eine Entscheidung durch die Wahl einer von n Aktionen mit für sie unbekanntem Typ. Dieser Typ bestimmt die möglicherweise verschiedenen, nicht-negativen Nutzwerte für S und R. Durch das Senden von Signalen versucht S, die Wahl von R zu beeinflussen. Wir betrachten zwei Grundszenarien: Bayesian Persuasion und Delegated Search.
In Bayesian Persuasion besitzt S Commitment Power. Hier legt sich S sich auf ein Signalschema φ fest und teilt dieses R mit. Es beschreibt, welches Signal S in welcher Situation sendet. Erst danach erfährt S den wahren Zustand der Welt. Nach Erhalt der durch φ bestimmten Signale wählt R eine der Aktionen. Das Wissen um φ erlaubt R die Annahmen über den Zustand der Welt in Abhängigkeit von den empfangenen Signalen zu aktualisieren. Dies muss S für das Design von φ berücksichtigen, denn R wird Empfehlungen nicht folgen, die S auf Kosten von R übervorteilen. Wir betrachten das Problem aus der Sicht von S und beschreiben Signalschemata, die S einen möglichst großen Nutzen garantieren.
Zuerst betrachten wir den Offline-Fall. Hier erfährt S den kompletten Zustand der Welt und schickt daraufhin ein Signal an R. Wir betrachten ein Szenario mit einer beschränkten Anzahl k ≤ n Signale. Mit nur k Signalen kann S höchstens k verschiedene Aktionen empfehlen. Für verschiedene symmetrische Instanzen beschreiben wir einen Polynomialzeitalgorithmus für die Berechnung eines optimalen Signalschemas mit k Signalen.
Weiterhin betrachten wir eine Teilmenge von Instanzen, in denen die Typen aus bekannten, unabhängigen Verteilungen gezogen werden. Wir beschreiben Polynomialzeitalgorithmen, die ein Signalschema mit k Signalen berechnen, das einen konstanten Approximationsfaktor im Verhältnis zum optimalen Signalschema mit k Signalen garantiert.
Im Online-Fall werden die Aktionstypen einzeln in Runden aufgedeckt. Nach Betrachtung der aktuellen Aktion sendet S ein Signal und R muss sofort durch Wahl oder Ablehnung der Aktion darauf reagieren. Der Prozess endet mit der Wahl einer Aktion. Andernfalls wird der nächste Aktionstyp aufgedeckt und vorherige Aktionen können nicht mehr gewählt werden. Als Richtwert für unsere Online-Signalschemata verwenden wir das beste Offline-Signalschema.
Zuerst betrachten wir ein Szenario mit unabhängigen Verteilungen. Wir zeigen, wie ein optimales Signalschema in Polynomialzeit bestimmt werden kann. Jedoch gibt es Beispiele, bei denen S – anders als im Offline-Fall – im Online-Fall keinen positiven Wert erzielen kann. Wir betrachten daraufhin eine Teilmenge der Instanzen, für die ein einfaches Signalschema einen konstanten Approximationsfaktor garantiert und zeigen dessen Optimalität.
Zusätzlich betrachten wir 16 verschiedene Szenarien mit unterschiedlichem Level an Information für S und R und unterschiedlichen Zielfunktionen für S und R unter der Annahme, dass die Aktionstypen a priori unbekannt sind, aber in uniform zufälliger Reihenfolge aufgedeckt werden. Für 14 Fälle beschreiben wir Signalschemata mit konstantem Approximationsfaktor. Solche Schemata existieren für die verbleibenden beiden Fälle nicht. Zusätzlich zeigen wir für die meistern Fälle, dass die beschriebenen Approximationsgarantien optimal sind.
Im zweiten Teil betrachten wir eine Online-Variante von Delegated Search. Hier besitzt nun R Commitment Power. Die Aktionstypen werden aus bekannten, unabhängigen Verteilungen gezogen. Bevor S die realisierten Typen beobachtet, legt R sich auf ein Akzeptanzschema φ fest. Für jeden Typen gibt φ an, mit welcher Wahrscheinlichkeit R diesen akzeptiert. Folglich versucht S, eine Aktion mit einem guten Typen für sich selbst zu finden, der von R akzeptiert wird. Da der Prozess online abläuft, muss S für jede Aktion einzeln entscheiden, diese vorzuschlagen oder zu verwerfen. Nur empfohlene Aktionen können von R ausgewählt werden.
Für den Offline-Fall sind für identisch verteilte Aktionstypen konstante Approximationsfaktoren im Vergleich zu einer Aktion mit optimalem Wert für R bekannt. Wir zeigen, dass R im Online-Fall im Allgemeinen nur eine Θ(1/n)-Approximation erzielen kann. Der Richtwert ist der erwartete Wert für eine eindimensionale Online-Suche von R.
Da für die Schranke eine exponentielle Diskrepanz in den Werten der Typen für S benötigt wird, betrachten wir parametrisierte Instanzen. Die Parameter beschränken die Werte für S bzw. das Verhältnis der Werte für R und S. Wir zeigen (beinahe) optimale logarithmische Approximationsfaktoren im Bezug auf diese Parameter, die von effizient berechenbaren Schemata garantiert werden.
The mobile games business is an ever-increasing sub-sector of the entertainment industry. Due to its high profitability but also high risk and competitive atmosphere, game publishers need to develop strategies that allow them to release new products at a high rate, but without compromising the already short lifespan of the firms' existing games. Successful game publishers must enlarge their user base by continually releasing new and entertaining games, while simultaneously motivating the current user base of existing games to remain active for more extended periods. Since the core-component reuse strategy has proven successful in other software products, this study investigates the advantages and drawbacks of this strategy in mobile games. Drawing on the widely accepted Product Life Cycle concept, the study investigates whether the introduction of a new mobile game built with core-components of an existing mobile game curtails the incumbent's product life cycle. Based on real and granular data on the gaming activity of a popular mobile game, the authors find that by promoting multi-homing (i.e., by smartly interlinking the incumbent and new product with each other so that users start consuming both games in parallel), the core-component reuse strategy can prolong the lifespan of the incumbent game.
Machine learning (ML) techniques have evolved rapidly in recent years and have shown impressive capabilities in feature extraction, pattern recognition, and causal inference. There has been an increasing attention to applying ML to medical applications, such as medical diagnosis, drug discovery, personalized medicine, and numerous other medical problems. ML-based methods have the advantage of processing vast amounts of data.
With an ever increasing amount of medical data collection and large, inter-subject variability in the medical data, automated data processing pipelines are very much desirable since it is laborious, expensive, and error-prone to rely solely on human processing. ML methods have the potential to uncover interesting patterns, unravel correlations between complex features, learn patient-specific representations, and make accurate predictions. Motivated by these promising aspects, in this thesis, I present studies where I have implemented deep neural networks for the early diagnosis of epilepsy based on electroencephalography (EEG) data and brain tumor detection based on magnetic resonance spectroscopy (MRS) data.
In the project for early diagnosis of epilepsy, we are dealing with one of the most common neurological disorders, epilepsy, which is characterized by recurrent unprovoked seizures. It can be triggered by a variety of initial brain injuries and manifests itself after a time window which is called the latent period. During this period, a cascade of structural and functional brain alterations takes place leading to an increased seizure susceptibility.
The development and extension of brain tissue capable of generating spontaneous seizures is defined as epileptogenesis (EPG).
Detecting the presence of EPG provides a precious opportunity for targeted early medical interventions and, thus, can slow down or even halt the disease progression. In order to study brain signals in this latent window, animal epilepsy models are used to provide valuable data as it is extremely difficult to obtain this data from human patients. The aim of this study is to discover biomarkers of EPG using animal models and then to find the equivalent and counterparts in human patients' data. However, the EEG features for EPG are not well-understood and there is not a sufficiently large amount of annotated data for ML-based algorithms. To approach this problem, firstly, I utilized the timestamp information of the recorded EEG from an animal epilepsy model where epilepsy is induced by an electrical stimulation. The timestamp serves as a form of weak supervision, i.e., before and after the stimulation. Secondly, I implemented a deep residual neural network and trained it with a binary classification task to distinguish the EEG signals from these two phases. After obtaining a high discriminative ability on the binary classification task, I proposed to divide further the time span after the stimulation for a three-class classification, aiming to detect possible stages of the progression of the latent EPG phase. I have shown that the model can distinguish EEG signals at different stages of EPG with high accuracy and generalization ability. I have also demonstrated that some of the learned features from the network are clinically relevant.
In the task of detecting brain tumors based on MRS data, I first proposed to apply a deep neural network on the MRS data collected from over 400 patients for a binary classification task. To combat the challenge of noisy labeling, I developed a distillation step to filter out relatively ``cleanly'' labeled samples. A mixing-based data augmentation method was also implemented to expand the size of the training set. All the experiments were designed to be conducted with a leave-patient-out scheme to ensure the generalization ability of the model. Averaged across all leave-patient-out cross-validation sets, the proposed method performed on par with human neuroradiologists, while outperforming other baseline methods. I have demonstrated the distillation effect on the MNIST data set with manually-introduced label noise as well as providing visualization of the input influences on the final classification through a class activation map method.
Moreover, I have proposed to aggregate information at the subject level, which could provide more information and insights. This is inspired by the concept of multiple instance learning, where instance-level labels are not required and which is more tolerant to noisy labeling. I have proposed to generate data bags consisting of instances from each patient and also proposed two modules to ensure permutation invariance, i.e., an attention module and a pooling module. I have compared the performance of the network in different cases, i.e., with and without permutation-invariant modules, with and without data augmentation, single-instance-based and multiple-instance-based learning and have shown that neural networks equipped with the proposed attention or pooling modules can outperform human experts.
What are the effects of the GDPR on consumer apps? This article presents an analysis of app behavior before and after the regulatory change in data protection in Europe. Based on long-term data collection, we present differences in app permission use and expressed user concerns and discuss their implications. In May 2018, the General Data Protection Regulation (GDPR) changed the data protection obligations of the information industry with the European Union users substantially. One should expect to find changes in code, program behavior and data collection activities. To investigate this expectation, we analyzed data about Android apps request and use of permissions to access sensitive group of data on smartphones, and collected user reviews. Our data shows an overall reduction of both permissions used and of expressed user concern. However, in some areas apps have increased access or user complaints while in addition, many apps carry with them several unused access privileges.
When we browse via WiFi on our laptop or mobile phone, we receive data over a noisy channel. The received message may differ from the one that was sent originally. Luckily it is often possible to reconstruct the original message but it may take a lot of time. That’s because decoding the received message is a complex problem, NP-hard to be exact. As we continue browsing, new information is sent to us in a high frequency. So if lags are to be avoided and as memory is finite, there is not much time left for decoding. Coding theory tackles this problem by creating models of the channels we use to communicate and tailor codes based on the channel properties. A well known family of codes are Low-Density Parity-Check codes (LDPC codes), they are widely used in standards like WiFi and DVB-T2. In practical settings the complexity of decoding a received message can be heavily reduced by using LDPC codes and approximative decoding algorithms. This thesis lays out the basic construction of LDPC codes and a proper decoding using the sum-product algorithm. On this basis a neural network to improve decoding is introduced. Therefore the sum-product algorithm is transformed into a neural network decoder. This approach was first presented by Nachmani et al. and treated in detail by Navneet Agrawal in 2017. To find out how machine learning can improve the codes, the bit error rates of the trained neural network decoder are compared with the bit error rates of the classic sum-product algorithm approach. Experiments with static and dynamic training datasets of diverse sizes, various signal-to-noise ratios, a feed forward as well as a recurrent architecture show how to tune the neural network decoder even further. Results of the experiments are used to verify statements made in Agrawal’s work. In addition, corrections and improvements in the area of metrics are presented. An implementation of the neural network to facilitate access for others will be made available to the public.
Digital distractions can interfere with goal attainment and lead to undesirable habits that are hard to get red rid of. Various digital self-control interventions promise support to alleviate the negative impact of digital distractions. These interventions use different approaches, such as the blocking of apps and websites, goal setting, or visualizations of device usage statistics. While many apps and browser extensions make use of these features, little is known about their effectiveness. This systematic review synthesizes the current research to provide insights into the effectiveness of the different kinds of interventions. From a search of the ‘ACM’, ‘Springer Link’, ‘Web of Science’, ’IEEE Xplore’ and ‘Pubmed’ databases, we identified 28 digital self-control interventions. We categorized these interventions according to their features and their outcomes. The interventions showed varying degrees of effectiveness, and especially interventions that relied purely on increasing the participants' awareness were barely effective. For those interventions that sanctioned the use of distractions, the current literature indicates that the sanctions have to be sufficiently difficult to overcome, as they will otherwise be quickly dismissed. The overall confidence in the results is low, with small sample sizes, short study duration, and unclear study contexts. From these insights, we highlight research gaps and close with suggestions for future research.
Enabling cybersecurity and protecting personal data are crucial challenges in the development and provision of digital service chains. Data and information are the key ingredients in the creation process of new digital services and products. While legal and technical problems are frequently discussed in academia, ethical issues of digital service chains and the commercialization of data are seldom investigated. Thus, based on outcomes of the Horizon2020 PANELFIT project, this work discusses current ethical issues related to cybersecurity. Utilizing expert workshops and encounters as well as a scientific literature review, ethical issues are mapped on individual steps of digital service chains. Not surprisingly, the results demonstrate that ethical challenges cannot be resolved in a general way, but need to be discussed individually and with respect to the ethical principles that are violated in the specific step of the service chain. Nevertheless, our results support practitioners by providing and discussing a list of ethical challenges to enable legally compliant as well as ethically acceptable solutions in the future.
In dieser Arbeit werden 4,6 Millionen englische Tweets, welche das Keyword „Bitcoin“ enthalten, analysiert und der Zusammenhang zwischen dem Sentiment der Tweets und den Renditen des Bitcoin untersucht. Zur Bestimmung der Sentiment-Klassen werden Text-Klassifizierer mit verschiedenen Ansätzen, darunter auch auf Convolutional Neural Networks und Transformern basierende Modelle, in diesem Kontext evaluiert und optimiert. Es wird außerdem ein Meta-Modell konstruiert, welches beim Problem der Sentiment-Klassifikation von Tweets in drei Klassen {Positiv, Negativ, Neutral} in der betrachteten Domäne besser abschneidet, als die anderen begutachteten Modelle. Bezüglich des Zusammenhangs wird im Speziellen auch der Einfluss von Merkmalen der Tweets und ihrer Verfassern anhand der Distanzkorrelation untersucht.
Consequences of minimal length discretization on line element, metric tensor, and geodesic equation
(2021)
When minimal length uncertainty emerging from a generalized uncertainty principle (GUP) is thoughtfully implemented, it is of great interest to consider its impacts on gravitational Einstein field equations (gEFEs) and to try to assess consequential modifications in metric manifesting properties of quantum geometry due to quantum gravity. GUP takes into account the gravitational impacts on the noncommutation relations of length (distance) and momentum operators or time and energy operators and so on. On the other hand, gEFE relates classical geometry or general relativity gravity to the energy–momentum tensors, that is, proposing quantum equations of state. Despite the technical difficulties, we intend to insert GUP into the metric tensor so that the line element and the geodesic equation in flat and curved space are accordingly modified. The latter apparently encompasses acceleration, jerk, and snap (jounce) of a particle in the quasi-quantized gravitational field. Finite higher orders of acceleration apparently manifest phenomena such as accelerating expansion and transitions between different radii of curvature and so on.
Our purpose was to analyze the robustness and reproducibility of magnetic resonance imaging (MRI) radiomic features. We constructed a multi-object fruit phantom to perform MRI acquisition as scan-rescan using a 3 Tesla MRI scanner. We applied T2-weighted (T2w) half-Fourier acquisition single-shot turbo spin-echo (HASTE), T2w turbo spin-echo (TSE), T2w fluid-attenuated inversion recovery (FLAIR), T2 map and T1-weighted (T1w) TSE. Images were resampled to isotropic voxels. Fruits were segmented. The workflow was repeated by a second reader and the first reader after a pause of one month. We applied PyRadiomics to extract 107 radiomic features per fruit and sequence from seven feature classes. We calculated concordance correlation coefficients (CCC) and dynamic range (DR) to obtain measurements of feature robustness. Intraclass correlation coefficient (ICC) was calculated to assess intra- and inter-observer reproducibility. We calculated Gini scores to test the pairwise discriminative power specific for the features and MRI sequences. We depict Bland Altmann plots of features with top discriminative power (Mann–Whitney U test). Shape features were the most robust feature class. T2 map was the most robust imaging technique (robust features (rf), n = 84). HASTE sequence led to the least amount of rf (n = 20). Intra-observer ICC was excellent (≥ 0.75) for nearly all features (max–min; 99.1–97.2%). Deterioration of ICC values was seen in the inter-observer analyses (max–min; 88.7–81.1%). Complete robustness across all sequences was found for 8 features. Shape features and T2 map yielded the highest pairwise discriminative performance. Radiomics validity depends on the MRI sequence and feature class. T2 map seems to be the most promising imaging technique with the highest feature robustness, high intra-/inter-observer reproducibility and most promising discriminative power.
This article presents the findings from systematically reviewing 26 empirical research studies published from 2005 to 2014 on the use of GIS for learning and teaching. By employing methods of narrative synthesis and qualitative content analysis, the study gives evidence about the state of knowledge of competence-based GIS education. The results explain what factors and variables effect GIS learning in terms of technology use, major subject contents, learning contexts, and didactic and pedagogical aspects. They also show what facets of knowledge, process skills, and affect the research literature has investigated. The analysis of the type and quality of the methods used indicates that current GIS education research is a heterogeneous field that needs a systematic research framework for future efforts, according to empirical education research.
An exploratory latent class analysis of student expectations towards learning analytics services
(2021)
For service implementations to be widely adopted, it is necessary for the expectations of the key stakeholders to be considered. Failure to do so may lead to services reflecting ideological gaps, which will inadvertently create dissatisfaction among its users. Learning analytics research has begun to recognise the importance of understanding the student perspective towards the services that could be potentially offered; however, student engagement remains low. Furthermore, there has been no attempt to explore whether students can be segmented into different groups based on their expectations towards learning analytics services. In doing so, it allows for a greater understanding of what is and is not expected from learning analytics services within a sample of students. The current exploratory work addresses this limitation by using the three-step approach to latent class analysis to understand whether student expectations of learning analytics services can clearly be segmented, using self-report data obtained from a sample of students at an Open University in the Netherlands. The findings show that student expectations regarding ethical and privacy elements of a learning analytics service are consistent across all groups; however, those expectations of service features are quite variable. These results are discussed in relation to previous work on student stakeholder perspectives, policy development, and the European General Data Protection Regulation (GDPR).
Szenen automatisch aus Texten generieren zu können ist eine interessante Aufgabe der Informatik. Für diese Aufgabe wurde VANNOTATOR (Mehler und Abrami 2019, Abrami, Spiekermann und Mehler 2019, Spiekermann, Abrami und Mehler 2018) entwickelt, ein Framework, das die Beschreibung bzw. Beschriftung von VR-Szenen ermöglicht. Damit für diese Szenen die benötigten 3D-Objekte bereitgestellt werden können, sind entsprechende Datenbanken vonnöten. Diese Datenbanken müssen umfangreich annotiert sein, damit diese Aufgabe bewältigt werden kann. Deshalb wurde im Falle des VANNOTATORs auf die ShapeNetSem Datenbank zurückgegriffen (Abrami, Henlein, Kett u. a. 2020).
Je detailreicher eine Szene dargestellt wird, desto detailreicher kann diese auch durch einen Text beschrieben werden. Aus diesem Grund wird die Datenbank um einen Teilbereich von PartNet (Mo u. a. 2019) erweitert. Dieser erlaubt die Option, Objekte zu segmentieren, und erweitert hierdurch das annotierbare Vokabular. Manche der bereits vorhandenen ShapeNetSem-Objekte verfügen über die Eigenschaft, dass sie auch PartNet-Objekte sind. Diese Arbeit befasst sich mit der Umsetzung, wie ShapeNetSem-Objekte mit hinterlegten PartNetObjekten durch diese ersetzt werden können. Um das zu bewerkstelligen, wurde ein Panel entworfen, in welchem ein PartNet-Objekt mit samt seinen einzelnen Segmenten aufgeführt wird. Diese Segmente können nun wie ShapeNetSem-Objekte ausgewählt und in einer Szene platziert werden. Dadurch werden 1.881 Objekte mit wiederum 34.016 Unterobjekten VANNOTATOR zur Verfügung gestellt. Dieses vergrößerte Vokabular hilft Natural Language Processing noch effektiver und präziser voranzutreiben.
Abstract: The human visual cortex enables visual perception through a cascade of hierarchical computations in cortical regions with distinct functionalities. Here, we introduce an AI-driven approach to discover the functional mapping of the visual cortex. We related human brain responses to scene images measured with functional MRI (fMRI) systematically to a diverse set of deep neural networks (DNNs) optimized to perform different scene perception tasks. We found a structured mapping between DNN tasks and brain regions along the ventral and dorsal visual streams. Low-level visual tasks mapped onto early brain regions, 3-dimensional scene perception tasks mapped onto the dorsal stream, and semantic tasks mapped onto the ventral stream. This mapping was of high fidelity, with more than 60% of the explainable variance in nine key regions being explained. Together, our results provide a novel functional mapping of the human visual cortex and demonstrate the power of the computational approach.
Author Summary: Human visual perception is a complex cognitive feat known to be mediated by distinct cortical regions of the brain. However, the exact function of these regions remains unknown, and thus it remains unclear how those regions together orchestrate visual perception. Here, we apply an AI-driven brain mapping approach to reveal visual brain function. This approach integrates multiple artificial deep neural networks trained on a diverse set of functions with functional recordings of the whole human brain. Our results reveal a systematic tiling of visual cortex by mapping regions to particular functions of the deep networks. Together this constitutes a comprehensive account of the functions of the distinct cortical regions of the brain that mediate human visual perception.
Der neue an der Goethe-Universität und dem FIAS konstruierte Supercomputer „L-CSC“ im Darmstädter GSI Helmholtzzentrums für Schwerionenforschung hat im weltweiten Vergleich der energiesparendsten Hochleistungscomputer den Weltmeistertitel errungen. Auf Anhieb erreichte der neue Höchstleistungsrechner Platz eins auf der am Donnerstag in New Orleans veröffentlichten Rangliste „Green500“, die weltweit die Energieeffizienz der schnellsten Supercomputer vergleicht. Mit einer Rechenleistung von 5,27 Milliarden Rechenoperationen pro Sekunde je Watt hat „L-CSC“ zugleich einen neuen Weltrekord für Energieeffizienz bei Supercomputern aufgestellt
Wie bewältigen wir die Datenflut? : Neues DFG-Schwerpunkt-Programm zu Algorithmen für Big Data
(2014)
Jeder pinnt ab und keiner merkt’s? Falsch, sagt der Fachbereich Rechtswissenschaft der Goethe-Universität. Seit gut sieben Jahren spüren Professoren und Dekanat systematisch Täuschungsversuche auf. Kein Leistungsnachweis von der Haus- und Seminar bis zur Examensarbeit, der nicht unter die Lupe genommen würde. Ein Computerprogramm macht’s möglich. Sobald es erste Hinweise liefert, fassen die Prüfer nach. „Die Software funktioniert wie eine Wünschelrute. Wir müssen dann graben“, beschreibt My-Sun Choi, die Leiterin des Referats Studienangelegenheiten die Suche. Die Beurteilung, ob echt oder falsch, wird nach Rücksprache mit der Professur getroffen.
The main topic of the present thesis is scene flow estimation in a monocular camera system. Scene flow describes the joint representation of 3D positions and motions of the scene. A special focus is placed on approaches that combine two kinds of information, deep-learning-based single-view depth estimation and model-based multi-view geometry.
The first part addresses single-view depth estimation focussing on a method that provides single-view depth information in an advantageous form for monocular scene flow estimation methods. A convolutional neural network, called ProbDepthNet, is proposed, which provides pixel-wise well-calibrated depth distributions. The experiments show that different strategies for quantifying the measurement uncertainty provide overconfident estimates due to overfitting effects. Therefore, a novel recalibration technique is integrated as part of the ProbDepthNet, which is validated to improve the calibration of the uncertainty measures. The monocular scene flow methods presented in the subsequent parts confirm that the integration of single-view depth information results in the best performance if the neural network provides depth distributions instead of single depth values and contains a recalibration.
Three methods for monocular scene flow estimation are presented, each one designed to combine multi-view geometry-based optimization with deep learning-based single-view depth estimation such as ProbDepthNet. While the first method, SVD-MSfM, performs the motion and depth estimation as two subsequent steps, the second method, Mono-SF, jointly optimizes the motion estimates and the depth structure. Both methods are tailored to address scenes, where the objects and motions can be represented by a set of rigid bodies. Dynamic traffic scenes are one kind of scenes that essentially fulfill this characteristic. The method, Mono-Stixel, uses an even more specialized scene model for traffic scenes, called stixel world, as underlying scene representation.
The proposed methods provide new state of the art for monocular scene flow estimation with Mono-SF being the first and leading monocular method on the KITTI scene flow benchmark at the time of submission of the present thesis. The experiments validate that both kind of information, the multi-view geometric optimization and the single-view depth estimates, contribute to the monocular scene flow estimates and are necessary to achieve the new state of the art accuracy.
Contemporary information systems make widespread use of artificial intelligence (AI). While AI offers various benefits, it can also be subject to systematic errors, whereby people from certain groups (defined by gender, age, or other sensitive attributes) experience disparate outcomes. In many AI applications, disparate outcomes confront businesses and organizations with legal and reputational risks. To address these, technologies for so-called “AI fairness” have been developed, by which AI is adapted such that mathematical constraints for fairness are fulfilled. However, the financial costs of AI fairness are unclear. Therefore, the authors develop AI fairness for a real-world use case from e-commerce, where coupons are allocated according to clickstream sessions. In their setting, the authors find that AI fairness successfully manages to adhere to fairness requirements, while reducing the overall prediction performance only slightly. However, they find that AI fairness also results in an increase in financial cost. Thus, in this way the paper’s findings contribute to designing information systems on the basis of AI fairness.
Die folgende Arbeit handelt von einer Text2Scene Anwendung, welche in der Virtual Reality (VR) umgesetzt wurde. Das System ermöglicht es den Usern aus einer Beschreibung einer Szene, diese virtuell nachzustellen. Dies bietet eine neue Art der Interaktion mit einem Text, die die visuelle Komponente hervorhebt und somit eine Geschichte auf neue Wege erfahrbar macht.
Dazu kann der User einen fertigen Text entweder vom Server zu laden oder einen eigenen erstellen, der dann automatisch verarbeitet wird. Dabei werden die vorhanden physischen Objekte im Text automatisch erkannt und dem User als 3D-Objekte in der virtuellen Umgebung zur Verfügung gestellt. Diese können dann manuell platziert werden und erzeugen dadurch die Szene, die im Ausgangstext beschrieben wurde. Das Ziel der Textverarbeitung ist eine möglichst genaue Beschreibung der Objekte, damit diese zielgerichtet in der Objektdatenbank gesucht werden können.
Bei der Textverarbeitung wird besonderer Wert auf das Erkennen von Teil-Ganz Beziehungen gelegt. Sodass Objekte, die im Text vorkommen und ein Holonym besitzen, automatisch mit diesem verknüpft werden. Gleichzeitig wird die Teil-Ganz Beziehung aber auch in die andere Richtung genauer betrachtet. Die Textverarbeitung soll ferner dazu in der Lage sein, Objekte genauer zu spezifizieren und an den Kontext des Textes anzupassen. Weiterhin wurde das Natural Language Processing (NLP) so ausgebaut, dass der Kontext des Textes erkannt wird und die Objekte entsprechend kategorisiert werden. Die Textverarbeitung wird mithilfe eines Neuronalen Netzes implementiert. Die verwendeten Tools zur Erkennung von Teil-Ganz Beziehungen, Kontext und Spezifikation von Objekten wurden anhand von Texteingaben nach der Genauigkeit der Ausgabe evaluiert.
Zur Nutzung der Textverarbeitung wurde eine virtuelle Szene entwickelt, die das Erstellen von eigenen Szenen aus vorher geladenen beziehungsweise eingegebenen Texten ermöglicht.
Dazu kann der Nutzer manuell oder automatisch Objekte laden lassen, die er dann platzieren kann.
Analysing survival or fixation probabilities for a beneficial allele is a prominent task in the field of theoretical population genetics. Haldane's asymptotics is an approximation for the fixation probability in the case of a single beneficial mutant with small selective advantage in a large population.
In this thesis we analyse the interplay between genetic drift and directional selection and prove Haldane's asymptotics in different settings: For the fixation probability in Cannings models with moderate selection and for the survival probability of a slightly supercritical branching processes in a random environment.
In Chapter 3 we introduce a class of Cannings models with selection that allow for a forward and backward construction. In particular, a Cannings ancestral selection process can be defined for this class of models, which counts the number of potential parents and is in sampling duality to the forward frequency process. By means of this duality the probability of fixation can be expressed through the expectation of the Cannings ancestral selection process in stationarity. A control of this expectation yields that the fixation probability fulfils Haldane's asymptotics in a regime of moderately weak selection (Thm. 8).
In Chapter 4 we study the fixation probability of Cannings models in a regime of moderately strong selection. Here couplings of the frequency process of beneficial individuals with slightly supercritical Galton-Watson processes imply that the fixation probability is given by Haldane's asymptotics (Thm. 9).
Lastly, in Chapter 5 we consider slightly supercritical branching processes in an independent and identically distributed random environment and study the probability of survival as the number of expected offspring tends from above to one. We show that only if variance and expectation of the random offspring mean are of the same order the random environment has a non-trivial influence on the probability of survival, which results in a modification of Haldane's asymptotics. Out of the critical parameter regime the population goes extinct or survives with a probability that fulfils Haldane's asymptotics (Thm. 10).
The proof establishes an expression for the survival probability in terms of the shape function of the random offspring generating functions. This expression exhibits similarities to perpetuities known from a financial context. Consequently, we prove a limiting theorem for perpetuities with vanishing interest rates (Thm. 11).
This work describes development of a comprehensive methodology for analyzing vibro-acoustic and wear mechanisms in transmission systems. The thesis addresses certain gaps present in the fields of structure dynamics and abrasion mechanism and opens new areas for further research.
The paper attempts to understand new and relatively unexplored challenges like influences of wear on the dynamics of drive train. It also focuses on developing new techniques for analyzing the vibration and acoustic behavior of the drive unit structures and surrounding fluids respectively.
The developed methodology meets the requirements of both the complete system and component level modeling by using specially identified combination of different simulation techniques. Based on the created template model, a three-stage spur plus helical gearbox is constructed and simulated as an application example. In addition to the internal mechanical excitation mechanisms, the transmission model also includes the rotational and translational dynamics of the gears, shafts and bearings. It is followed by illustration of wear among the rotating components.
Different kinds of static and dynamic analyses are performed and coupled at various levels depending on the mechanical complexities involved. Furthermore, the structure dynamic vibration of the housing and the associated sound particle radiations are mapped into the surrounding fluid. Additionally, the approach for selection of the potential parameters for optimization is depicted. Final part focuses on the measurements of different system states used for validation of the model. In the end, results obtained from both simulations and experiments are analyzed and assessed for there respective performances.
This study explores how ‘gatherings’ turn into ‘encounters’ in a virtual world (VW) context. Most communication technologies enable only focused encounters between distributed participants, but in VWs both gatherings and encounters can occur. We present close sequential analysis of moments when after a silent gathering, interaction among participants in a VW is gradually resumed, and also investigate the social actions in the verbal (re-)opening turns. Our findings show that like in face-to-face situations, also in VWs participants often use different types of embodied resources to achieve the transition, rather than rely on verbal means only. However, the transition process in VWs has distinctive characteristics compared to the one in face-to-face situations. We discuss how participants in a VW use virtually embodied pre-beginnings to display what we call encounter-readiness, instead of displaying lack of presence by avatar stillness. The data comprise 40 episodes of video-recorded team interactions in a VW.
The paper presents research results emerging from the analysis of Intelligent Personal Assistants (IPA) log data. Based on the assumption that media and data, as part of practice, are produced and used cooperatively, the paper discusses how IPA log data can be used to analyze (1) how the IPA systems operate through their connection to platforms and infrastructures, (2) how the dialog systems are designed today and (3) how users integrate them into their everyday social interaction. It also asks in which everyday practical contexts the IPA are placed on the system side and on the user side, and how privacy issues in particular are negotiated. It is argued that, in order to be able to investigate these questions, the technical-institutional and the cultural-theoretical perspective on media, which is common in German media linguistics, has to be complemented by a more fundamental, i.e. social-theoretical and interactionist perspective.
Jeden Tag werden 2,5 Trillionen Bytes an Daten generiert. Diese enorme Menge an Daten wird beispielsweise durch digitale Bilder, Videos, Beiträge in den sozialen Medien, intelligente Sensoren, Einzelhandels- und Finanztransaktionen und GPS-Signale von Handys erzeugt. Das ist Big Data. Es besteht kein Zweifel daran, dass Big Data und das, was wir damit tun, das Potential hat, ein signifikanter Treiber für Innovationen und Wertschöpfung zu werden.
Multi-view microscopy techniques are used to increase the resolution along the optical axis for 3D imaging. Without this, the resolution is insufficient to resolve subcellular events. In addition, parts of the images of opaque specimens are often highly degraded or masked. Both problems motivate scientists to record the same specimen from multiple directions. The images, then have to be digitally fused into a single high-quality image. Selective-plane illumination microscopy has proven to be a powerful imaging technique due to its unsurpassed acquisition speed and gentle optical sectioning. However, even in the case of multi view imaging techniques that illuminate and image the sample from multiple directions, light scattering inside tissues often severely impairs image contrast.
Here we show that for c-elegans embryos multi view registration can be achieved based on segmented nuclei. However, segmentation of nuclei in high density distribution like c-elegans embryo is challenging. We propose a method which uses 3D Mexican hat filter for preprocessing and 3D Gaussian curvature for the post-processing step to separate nuclei. We used this method successfully on 3 data sets of c-elegans embryos in 3 different views. The result of segmentation outperforms previous methods. Moreover, we provide a simple GUI for manual correction and adjusting the parameters for different data.
We then proposed a method that combines point and voxel registration for an accurate multi view reg- istration of c-elegans embryo, which does not need any special experimental preparation. We demonstrate the performance of our approach on data acquired from fixed embryos of c-elegans worms. This multi step approach is successfully evaluated by comparison to different methods and also by using synthetic data. The proposed method could overcome the typically low resolution along the optical axis and enable stitching to- gether the different parts of the embryo available through the different views. A tool for running the code and analyzing the results is developed.
Im Projekt »AI and digital Technology in Learning and Instruction« (ALI) soll ein interdisziplinär geprägtes Studienangebot zum Einsatz von Künstlicher Intelligenz und digitaler Technologien in Bildungsprozessen entwickelt werden. Prof. Holger Horz, Pädagogischer Psychologe an der Goethe-Universität und Leiter des Projekts, erläutert die bildungswissenschaftlichen, informatischen und kulturellen Hintergründe und Zielsetzungen von ALI, das aus dem Bund-Länder-Programm »KI in der Hochschulbildung« gefördert werden wird.
Correctness of program transformations and translations in concurrent programming is the focus of our research. In this case study the relation of the synchronous pi-calculus and a core language of Concurrent Haskell (CH) with asynchronous communication is investigated. We show that CH embraces the synchronous pi-calculus. The formal foundations are contextual semantics in both languages, where may- as well as should-convergence are observed. We succeed in defining and proving smart properties of a particular translation mapping the synchronous pi-calculus into CH. This implies that pi-processes are error-free if and only if their translation is an error-free CH-program Our result shows that the chosen semantics is not only powerful, but can also be applied in concrete and technically complex situations. The developed translation uses private names. We also automatically check potentially correct translations that use global names instead of private names. As a complexity parameter we use the number of MVars introduced by the transformation, where MVars are synchronized 1-place buffers. The automated refutation of incorrect translations leads to a classification of potentially correct translations, and to the conjecture that one global MVar is insufficient.
Correctness of program transformations and translations in concurrent programming is the focus of our research. In this case study the relation of the synchronous pi-calculus and a core language of Concurrent Haskell (CH) with asynchronous communication is investigated. We show that CH embraces the synchronous pi-calculus. The formal foundations are contextual semantics in both languages, where may- as well as should-convergence are observed. We succeed in defining and proving smart properties of a particular translation mapping the synchronous pi-calculus into CH. This implies that pi-processes are error-free if and only if their translation is an error-free CH-program Our result shows that the chosen semantics is not only powerful, but can also be applied in concrete and technically complex situations. The developed translation uses private names. We also automatically check potentially correct translations that use global names instead of private names. As a complexity parameter we use the number of MVars introduced by the transformation, where MVars are synchronized 1-place buffers. The automated refutation of incorrect translations leads to a classification of potentially correct translations, and to the conjecture that one global MVar is insufficient.
Correctness of program transformations and translations in concurrent programming is the focus of our research. In this case study the relation of the synchronous pi-calculus and a core language of Concurrent Haskell (CH) with asynchronous communication is investigated. We show that CH embraces the synchronous pi-calculus. The formal foundations are contextual semantics in both languages, where may- as well as should-convergence are observed. We succeed in defining and proving smart properties of a particular translation mapping the synchronous pi-calculus into CH. This implies that pi-processes are error-free if and only if their translation is an error-free CH-program Our result shows that the chosen semantics is not only powerful, but can also be applied in concrete and technically complex situations. The developed translation uses private names. We also automatically check potentially correct translations that use global names instead of private names. As a complexity parameter we use the number of MVars introduced by the transformation, where MVars are synchronized 1-place buffers. The automated refutation of incorrect translations leads to a classification of potentially correct translations, and to the conjecture that one global MVar is insufficient.
We investigate translations from the synchronous pi-calculus
into a core language of Concurrent Haskell (CH). Synchronous messagepassing of the pi-calculus is encoded as sending messages and adding synchronization using Concurrent Haskell’s mutable shared-memory locations (MVars). Our correctness criterion for translations is invariance of may- and should-convergence. This embraces that all executions of a process are error-free if and only if this also holds for the translated program. We exhibit a particular correct translation that uses a fresh, private MVar per communication interaction and that is in addition adequate, and which is also fully abstract on closed expressions. A metaresult is that CH has the expressive power and the concurrency capabilities of the synchronous pi-calculus.
We also automatically check variants of translations of synchronous communication into an asynchronous calculus where only an a priori fixed number of MVars per channel (and not per communication interaction!) is available. We obtain non-correctness results for classes of small translations, and exemplary argue for the correctness (and adequacy) for two translations with a higher number of MVars. We introduce a classification of the potentially correct translations.
We consider matching, rewriting, critical pairs and the Knuth-Bendix confluence test on rewrite rules in a nominal setting extended by atom-variables. Computing critical pairs is done using nominal unification, and rewriting using nominal matching. We utilise atom-variables to formulate rewrite rules, which is an improvement over previous approaches, using usual nominal unification, nominal matching and nominal equivalence of expressions coupled with a freshness constraint. We determine the complexity of several problems in a quantified freshness logic. In particular we show that nominal matching is Πp2-complete. We prove that the adapted Knuth-Bendix confluence test is applicable to a nominal rewrite system with atom-variabes and thus, that there is a decidable test whether confluence of the ground instance of the abstract rewrite system holds. We apply the nominal Knuth Bendix confluence criterion to the theory of monads, and compute a convergent nominal rewrite system modulo alpha-equivalence.
A sound and complete algorithm for nominal unification of higher-order expressions with a recursive let is described, and shown to run in non-deterministic polynomial time. We also explore specializations like nominal letrec-matching for expressions, for DAGs, and for garbage-free expressions and determine their complexity. As extension a nominal unification algorithm for higher-order expressions with recursive let and atom-variables is constructed, where we show that it also runs in non-deterministic polynomial time.
Machine Learning (ML) is so pervasive in our todays life that we don't even realise that, more often than expected, we are using systems based on it. It is also evolving faster than ever before. When deploying ML systems that make decisions on their own, we need to think about their ignorance of our uncertain world. The uncertainty might arise due to scarcity of the data, the bias of the data or even a mismatch between the real world and the ML-model. Given all these uncertainties, we need to think about how to build systems that are not totally ignorant thereof. Bayesian ML can to some extent deal with these problems. The specification of the model using probabilities provides a convenient way to quantify uncertainties, which can then be included in the decision making process.
In this thesis, we introduce the Bayesian ansatz to modeling and apply Bayesian ML models in finance and economics. Especially, we will dig deeper into Gaussian processes (GP) and Gaussian process latent variable model (GPLVM). Applied to the returns of several assets, GPLVM provides the covariance structure and also a latent space embedding thereof. Several financial applications can be build upon the output of the GPLVM. To demonstrate this, we build an automated asset allocation system, a predictor for missing asset prices and identify other structure in financial data.
It turns out that the GPLVM exhibits a rotational symmetry in the latent space, which makes it harder to fit. Our second publication reports, how to deal with that symmetry. We propose another parameterization of the model using Householder transformations, by which the symmetry is broken. Bayesian models are changed by reparameterization, if the prior is not changed accordingly. We provide the correct prior distribution of the new parameters, such that the model, i.e. the data density, is not changed under the reparameterization. After applying the reparametrization on Bayesian PCA, we show that the symmetry of nonlinear models can also be broken in the same way.
In our last project, we propose a new method for matching quantile observations, which uses order statistics. The use of order statistics as the likelihood, instead of a Gaussian likelihood, has several advantages. We compare these two models and highlight their advantages and disadvantages. To demonstrate our method, we fit quantiled salary data of several European countries. Given several candidate models for the fit, our method also provides a metric to choose the best option.
We hope that this thesis illustrates some benefits of Bayesian modeling (especially Gaussian processes) in finance and economics and its usage when uncertainties are to be quantified.
In the last two decades, our understanding of human gene regulation has improved tremendously. There are plentiful computational methods which focus on integrative data analysis of humans, and model organisms, like mouse and drosophila. However, these tools are not directly employable by researchers working on non-model organisms to answer fundamental biological, and evolutionary questions. We aimed to develop new tools, and adapt existing software for the analysis of transcriptomic and epigenomic data of one such non-model organism, Paramecium tetraurelia, an unicellular eukaryote. Paramecium contains two diploid (2n) germline micronuclei (MIC) and a polyploid (800n) somatic macronuclei (MAC). The transcriptomic and epigenomic regulatory landscape of the MAC genome, which has 80% protein-coding genes and short intergenic regions, is poorly understood.
We developed a generic automated eukaryotic short interfering RNA (siRNA) analysis tool, called RAPID. Our tool captures diverse siRNA characteristics from small RNA sequencing data and provides easily navigable visualisations. We also introduced a normalisation technique to facilitate comparison of multiple siRNA-based gene knockdown studies. Further, we developed a pipeline to characterise novel genome-wide endogenous short interfering RNAs (endo-siRNAs). In contrary to many organisms, we found that the endo-siRNAs are not acting in cis, to silence their parent mRNA. We also predicted phasing of siRNAs, which are regulated by the RNA interference (RNAi) pathway.
Further, using RAPID, we investigated the aberrations of endo-siRNAs, and their respective transcriptomic alterations caused by an RNAi pathway triggered by feeding small RNAs against a target gene. We find that the small RNA transcriptome is altered, even if a gene unrelated to RNAi pathway is targeted. This is important in the context of investigations of genetically modified organisms (GMOs). We suggest that future studies need to distinguish transcriptomic changes caused by RNAi inducing techniques and actual regulatory changes.
Subsequently, we adapted existing epigenomics analysis tools to conduct the first comprehensive epigenomic characterisation of nucleosome positioning and histone modifications of the Paramecium MAC. We identified well positioned nucleosomes shifted downstream of the transcription start site. GC content seems to dictate, in cis, the positioning of nucleosomes, histone marks (H3K4me3, H3K9ac, and H3K27me3), and Pol II in the AT-rich Paramecium genome. We employed a chromatin state segmentation approach, on nucleosomes and histone marks, which revealed genes with active, repressive, and bivalent chromatin states. Further, we constructed a regulatory association network of all the aforementioned data, using the sparse partial correlation network technique. Our analysis revealed subsets of genes, whose expression is positively associated with H3K27me3, different to the otherwise reported negative association with gene expression in many other organisms.
Further, we developed a Random Forests classifier to predict gene expression using genic (gene length, intron frequency, etc.) and epigenetic features. Our model has a test performance (PR-AUC) of 0.83. Upon evaluating different feature sets, we found that genic features are as predictive, of gene expression, as the epigenetic features. We used Shapley local feature explanation values, to suggest that high H3K4me3, high intron frequency, low gene length, high sRNA, and high GC content are the most important elements for determining gene expression status.
In this thesis, we developed novel tools, and employed several bioinformatics and machine learning methods to characterise the regulatory landscape of the Paramecium’s (epi)genome.
The aim of this study was to identify and evaluate different de-identification techniques that may be used in several mobility-related use cases. To do so, four use cases have been defined in accordance with a project partner that focused on the legal aspects of this project, as well as with the VDA/FAT working group. Each use case aims to create different legal and technical issues with regards to the data and information that are to be gathered, used and transferred in the specific scenario. Use cases should therefore differ in the type and frequency of data that is gathered as well as the level of privacy and the speed of computation that is needed for the data. Upon identifying use cases, a systematic literature review has been performed to identify suitable de-identification techniques to provide data privacy. Additionally, external databases have been considered as data that is expected to be anonymous might be reidentified through the combination of existing data with such external data.
For each case, requirements and possible attack scenarios were created to illustrate where exactly privacy-related issues could occur and how exactly such issues could impact data subjects, data processors or data controllers. Suitable de-identification techniques should be able to withstand these attack scenarios. Based on a series of additional criteria, de-identification techniques are then analyzed for each use case. Possible solutions are then discussed individually in chapters 6.1 - 6.2. It is evident that no one-size-fits-all approach to protect privacy in the mobility domain exists. While all techniques that are analyzed in detail in this report, e.g., homomorphic encryption, differential privacy, secure multiparty computation and federated learning, are able to successfully protect user privacy in certain instances, their overall effectiveness differs depending on the specifics of each use case.
Chatbots are a promising technology with the potential to enhance workplaces and everyday life. In terms of scalability and accessibility, they also offer unique possibilities as communication and information tools for digital learning. In this paper, we present a systematic literature review investigating the areas of education where chatbots have already been applied, explore the pedagogical roles of chatbots, the use of chatbots for mentoring purposes, and their potential to personalize education. We conducted a preliminary analysis of 2,678 publications to perform this literature review, which allowed us to identify 74 relevant publications for chatbots’ application in education. Through this, we address five research questions that, together, allow us to explore the current state-of-the-art of this educational technology. We conclude our systematic review by pointing to three main research challenges: 1) Aligning chatbot evaluations with implementation objectives, 2) Exploring the potential of chatbots for mentoring students, and 3) Exploring and leveraging adaptation capabilities of chatbots. For all three challenges, we discuss opportunities for future research.
The sketch map tool facilitates the assessment of OpenStreetMap data for participatory mapping
(2021)
A worldwide increase in the number of people and areas affected by disasters has led to more and more approaches that focus on the integration of local knowledge into disaster risk reduction processes. The research at hand shows a method for formalizing this local knowledge via sketch maps in the context of flooding. The Sketch Map Tool enables not only the visualization of this local knowledge and analyses of OpenStreetMap data quality but also the communication of the results of these analyses in an understandable way. Since the tool will be open-source and several analyses are made automatically, the tool also offers a method for local governments in areas where historic data or financial means for flood mitigation are limited. Example analyses for two cities in Brazil show the functionalities of the tool and allow the evaluation of its applicability. Results depict that the fitness-for-purpose analysis of the OpenStreetMap data reveals promising results to identify whether the sketch map approach can be used in a certain area or if citizens might have problems with marking their flood experiences. In this way, an intrinsic quality analysis is incorporated into a participatory mapping approach. Additionally, different paper formats offered for printing enable not only individual mapping but also group mapping. Future work will focus on advancing the automation of all steps of the tool to allow members of local governments without specific technical knowledge to apply the Sketch Map Tool for their own study areas.
We empirically investigate algorithms for solving Connected Components in the external memory model. In particular, we study whether the randomized O(Sort(E)) algorithm by Karger, Klein, and Tarjan can be implemented to compete with practically promising and simpler algorithms having only slightly worse theoretical cost, namely Borůvka’s algorithm and the algorithm by Sibeyn and collaborators. For all algorithms, we develop and test a number of tuning options. Our experiments are executed on a large set of different graph classes including random graphs, grids, geometric graphs, and hyperbolic graphs. Among our findings are: The Sibeyn algorithm is a very strong contender due to its simplicity and due to an added degree of freedom in its internal workings when used in the Connected Components setting. With the right tunings, the Karger-Klein-Tarjan algorithm can be implemented to be competitive in many cases. Higher graph density seems to benefit Karger-Klein-Tarjan relative to Sibeyn. Borůvka’s algorithm is not competitive with the two others.
This thesis presents research which spans three conference papers and one manuscript which has not yet been submitted for peer review.
The topic of 1 is the inherent complexity of maintaining perfect height in B-trees. We consider the setting in which a B-tree of optimal height contains n = (1−ϵ)N elements where N is the number of elements in full B-tree of the same height (the capacity of the tree). We show that the rebalancing cost when updating the tree—while maintaining optimal height—depends on ϵ. Specifically, our analysis gives a lower bound for the rebalancing cost of Ω(1/(ϵB)). We then describe a rebalancing algorithm which has an amortized rebalancing cost with an almost matching upper bound of O(1/(ϵB)⋅log²(min{1/ϵ,B})). We additionally describe a scheme utilizing this algorithm which, given a rebalancing budget f(n), maintains optimal height for decreasing ϵ until the cost exceeds the
budget at which time it maintains optimal height plus one. Given a rebalancing budget of Θ(logn), this scheme maintains optimal height for all but a vanishing fraction of sizes in the intervals between tree capacities.
Manuscript 2 presents empirical analysis of practical randomized external-memory algorithms for computing the connected components of graphs. The best known theoretical results for this problem are essentially all derived from results for minimum spanning tree algorithms. In the realm of randomized external-memory MST algorithms, the best asymptotic result has I/O-complexity O(sort(|E|)) in expectation while an empirically studied practical algorithm has a bound of O(sort(|E|)⋅log(|V|/M)). We implement and evaluate an algorithm for connected components with expected I/O-complexity O(sort(|E|))—a simplification of the MST
algorithm with this asymptotic cost, we show that this approach may also yield good results in practice.
In paper 3, we present a novel approach to simulating large-scale population protocol models. Naive simulation of N interactions of a population protocol with n agents and m states requires Θ(nlogm) bits of memory and Θ(N) time. For
very large n, this is prohibitive both in memory consumption and time, as interesting protocols will typically require N > n interactions for convergence. We describe a histogram-based simulation framework which requires Θ(mlogn) bits of memory instead—an improvement as it is typically the case that
n ≫ m. We analyze, implement, and compare a number of different data structures to perform correct agent sampling in this regime. For this purpose, we develop dynamic alias tables which allow sampling an interaction in expected amortized
constant time. We then show how to use sampling techniques to process agent interactions in batches, giving a simulation approach which uses subconstant time per interaction under reasonable assumptions.
With paper 4, we introduce the new model of fragile complexity for comparison-based algorithms. Within this model, we analyze classical comparison-based problems such as finding the minimum value of a set, selection (or finding the median), and sorting. We prove a number of lower and upper bounds and in particular, we give a number of randomized results which describe trade-offs not achievable by deterministic algorithms.
Dieser Arbeit war zum Ziel gesetzt, Methoden zur Simulation von neuronalen Prozessen zu entwickeln, zu implementieren, einzusetzen und zu vergleichen. Ein besonderes Augenmerk lag dabei auf der Frage, wo eine volle räumliche Auflösung der Modelle benötigt wird und wo darauf zugunsten von vereinfachenden niederdimensionalen Modellen, die wesentlich weniger Ressourcen und mathematischen Sachverstand erfordern, verzichtet werden kann. Außerdem wurde speziell bei der Beschreibung der verschiedenen Modelle für die Elektrik der Nervenzellen das Anliegen verfolgt, deren Zusammenhänge und die Natur vereinfachender Annahmen herauszuarbeiten, um deutlich zu machen, an welchen Stellen Probleme bei der Benutzung der weniger komplexen Modelle auftreten können.
In etlichen Beispielen wurde daraufhin untersucht, inwieweit die Vereinfachung auf ein eindimensionales Kabelmodell sowie der Verzicht auf die Betrachtung einzelner Ionensorten die realistische Darstellung der zellulären Elektrik beeinträchtigen können. Dabei stellte sich heraus, dass alle betrachteten Modelle für das rein elektrische Verhalten der Neuronen im Wesentlichen dieselben Ergebnisse liefern, weshalb zu dessen Simulation in den allermeisten Fällen ein 1D-Kabelmodell völlig ausreichend und angezeigt sein dürfte.
Nur wenn Größen von Interesse sind, die in diesem Modell nicht erfasst werden, etwa das Außenraumpotential oder die Ionenkonzentrationen, muss auf genauere Modelle zurückgegriffen werden. Außerdem ist in einer Konvergenzstudie exemplarisch vorgeführt worden, dass bereits eine recht grobe Darstellung der zugrundeliegenden Rechengitter genügt, um korrekte Ergebnisse bei der Simulation der rein elektrischen Signale sicherzustellen.
In scharfem Kontrast steht hierzu die Simulation von einzelnen Ionen-Dynamiken. Bereits in der Untersuchung des Poisson-Nernst-Planck-Modells für das Membranpotential erwies sich, dass für eine korrekte Simulation der diffusiven Anteile der Ionenbewegung wesentlich feinere Gitter benötigt werden.
Noch viel deutlicher wurde dies in Simulationen von Calcium-Wellen in Dendriten, wo -- neben anderen Einsichten -- aufgezeigt werden konnte, dass nicht nur eine feine axiale
(und Zeit-) Auflösung der Dendritengeometrie zur Sicherstellung exakter Ergebnisse notwendig ist, sondern auch die räumliche Auflösung in die übrigen Dimensionen wichtig ist, weswegen eine eindimensionale Kabeldarstellung der Calcium-Dynamik erheblich fehlerbehaftet und
(jedenfalls im Zusammenhang mit Ryanodin-Rezeptorkanälen) von deren Nutzung dringend abzuraten ist. Auch die Darstellung von Kanälen als eine kontinuierliche Dichte in der Membran kann, wie darüber hinaus vorgeführt wurde, problematisch sein.
Ihre exaktere Modellierung, etwa durch Einbettung auch probabilistischer Einzelkanaldarstellungen in das räumliche Modell sollte in zukünftigen Arbeiten noch mehr thematisiert werden.
Mit Blick auf die Wiederverwendbarkeit bereits implementierter Funktionalität innerhalb dieser Arbeiten wurden spezielle Teile dieser Funktionalität hier in einem gesonderten
Kapitel genauer beschrieben. Als komplexes Beispiel für das, was simulationstechnisch bereits im Bereich des Machbaren
liegt, und gleichsam für eine Anwendung, die zeigt, wie möglichst viele der im Rahmen dieser Arbeit entwickelten Methoden miteinander kombiniert werden können, wurde die
Calcium-Dynamik eines kompletten Dendriten innerhalb eines großen aktiven neuronalen Netzwerks simuliert.
Um Wissen in einer Form abzulegen, in der es automatisiert verarbeitet werden kann, werden unter anderem Ontologien verwendet. Ontologien erlauben über einen als Inferenz bezeichneten Prozess die Ableitung neuen Wissens. Bei inhaltlichen Überschneidungen werden Ontologien über Ontologie-Alignments miteinander verbunden, die Entitäten aus den verschiedenen Ontologien in Beziehung zueinander setzen. Üblicherweise werden diese Alignments als Mengen von Äquivalenzen formuliert, die beschreiben, welche Konzepte aus einer Ontologie Konzepten aus einer anderen Ontologie entsprechen. Ebenfalls verbreitet sind Ober- und Unterklassenbeziehungen in Alignments.
Diese Ontologie-Alignments werden zum Beispiel in der Biomedizin in Forschungsdatenbanken verwendet, da durch Alignments Informationen aus verschiedenen Bereichen zusammengeführt werden können. Der manuelle Aufwand, um große Ontologien und Alignments zu erstellen, ist sehr hoch. Dementsprechend wäre es wünschenswert, bei einer Veränderung von Ontologien nicht wieder von vorne beginnen und eine neue Ontologie erstellen zu müssen und möglichst viel aus der veränderten Ontologie und den die Ontologie betreffenden Alignments wiederverwenden zu können. Daher sollten möglichst automatisierte Verfahren verwendet werden. Diese Arbeit untersucht vier Ansätze, um die Anpassung von Alignments an Veränderungen in Ontologien zu automatisieren.
Der erste Ansatz bezieht Inferenzen in den Prozess zur Vorhersage von Alignment-Änderungen mit ein. Dazu werden die Inferenzen vor und nach der Änderung der Ontologien berechnet und auf Basis der Unterschiede mit einem regelbasierten Algorithmus bestimmt, wie sich das Alignment ändern soll. Der zweite Ansatz, wie auch die weiteren Ansätze, hat nicht zum Ziel das Alignment direkt anzupassen. Stattdessen soll vorhergesagt werden, welche Teile des Alignments angepasst werden müssen. Dazu werden die Ontologien und das Alignment als Wissensgraph-Embeddings repräsentiert. Diese Embeddings bilden Knoten aus den Ontologien in einen Raum mit 300-1000 Dimensionen so ab, dass in dem Raum auch die Beziehungen zwischen den Entitäten der Ontologien repräsentiert werden können. Diese Embeddings werden dann verwendet, um verschiedene Klassifikationsalgorithmen zu trainieren. Auf diese Weise wird vorhergesagt, welche Teile des Alignments sich verändern werden. Der dritte Ansatz verbindet Embeddings mit einem Veränderungsmodell. Das Veränderungsmodell kategorisiert die an den Ontologien vorgenommenen Veränderungen. Auf diese Kategorisierung und das Embedding werden dann Klassifikationsalgorithmen angewandt. Der vierte Ansatz verwendet eine speziell auf Wissensgraphen ausgerichtete Architektur für neuronale Netze, sogenannte Graph Convolutional Networks, um Veränderungen an Alignments vorher zu sagen.
Diese Ansätze werden auf ihre jeweiligen Vor- und Nachteile untersucht. Dazu werden die Verfahren an zwei Anwendungsfällen untersucht. Der Ansatz zur regelbasierten Einbeziehung von Inferenzen wird anhand eines Anwendungsbeispiels aus dem Bereich der Interweaving Systems betrachtet. In dem Beispiel wird eine allgemeine Methode für Interweaving Systems angewandt um das Selbstmanagement von Ampelsteuerungen zu ermöglichen. Die auf maschinellem Lernen aufbauenden Ansätze werden auf einem Auszug aus der biomedizinischen Forschungsdatenbank UMLS evaluiert.
Dabei konnte festgestellt werden, dass die betrachteten Ansätze grundsätzlich zur Anpassung von Alignments an Ontologie-Veränderungen eingesetzt werden können. Der Ansatz zur regelbasierten Einbeziehung von Inferenzen kann dabei vor allem auf sehr kleinen Datensätzen eingesetzt werden, bei denen alle Gesetzmäßigkeiten der Veränderungen grundsätzlich bekannt sind. Diese Anwendbarkeit ergibt sich aus dem Entwurf der Problemstellung für den ersten Ansatz. Die auf maschinellem Lernen aufbauenden Ansätze eignen sich besonders für große Datensätze und bieten den Vorteil, dass auch ohne ein vollständiges Verständnis des Veränderungsprozesses Vorhersagen getroffen werden können.
Unter den Ansätzen, die maschinelles Lernen einsetzen, zeigte die Einbeziehung von Veränderungsmodellen keine Vorteile gegenüber den anderen Ansätzen. Auf einem etwas
kleineren Datensatz waren die Ergebnisse des Embedding-basierten Ansatzes und der Relational Graph Convolutional Networks vergleichbar, während auf einem größeren Datensatz
die Graph Convolutional Networks etwas bessere Ergebnisse erreichen konnten.
Weitere Ergebnisse dieser Arbeit stellen eine Formalisierung der Problemstellung der Anpassung von Ontologie-Alignments an Veränderungen sowie eine formale Darstellung der Ansätze dar. Ein weiterer Beitrag der Arbeit ist die Vorstellung eines Anwendungsfalls aus dem Bereich der Interweaving Systems für Ontologie-Alignments. Außerdem wurde das Problem der Anpassung von Alignments an Veränderungen so formuliert, dass es mithilfe von
maschinellem Lernen betrachtet werden kann.
Principles of cognitive maps
(2021)
This thesis analyses the concept of a cognitive map in the research fields of geography. Cognitive mapping research is essential as it investigates the relations between cognitive maps and external representations of space that people regularly use by acquiring spatial knowledge, such as maps in geographic information systems. Moreover, cognitive maps, when expanded on semantic maps, explain the relations between people and things in a non-physically environment, where the considered space is not spanned by distance but with other non-spatially variables. Nevertheless, cognitive maps are often distorted. Although a good formation of a cognitive map is vital in navigation processes, cognitive distortions are barely investigated in the field of geography. By analyzing the relevant work, especially Tobler’s first law of geography, a new lexical variant of Tobler’s first law could be stated that could presumably describe a specific distortion in the processing of landmarks in cognitive maps.
Deep learning with neural networks seems to have largely replaced traditional design of computer vision systems. Automated methods to learn a plethora of parameters are now used in favor of previously practiced selection of explicit mathematical operators for a specific task. The entailed promise is that practitioners no longer need to take care of every individual step, but rather focus on gathering big amounts of data for neural network training. As a consequence, both a shift in mindset towards a focus on big datasets, as well as a wave of conceivable applications based exclusively on deep learning can be observed.
This PhD dissertation aims to uncover some of the only implicitly mentioned or overlooked deep learning aspects, highlight unmentioned assumptions, and finally introduce methods to address respective immediate weaknesses. In the author’s humble opinion, these prevalent shortcomings can be tied to the fact that the involved steps in the machine learning workflow are frequently decoupled. Success is predominantly measured based on accuracy measures designed for evaluation with static benchmark test sets. Individual machine learning workflow components are assessed in isolation with respect to available data, choice of neural network architecture, and a particular learning algorithm, rather than viewing the machine learning system as a whole in context of a particular application. Correspondingly, in this dissertation, three key challenges have been identified: 1. Choice and flexibility of a neural network architecture. 2. Identification and rejection of unseen unknown data to avoid false predictions. 3. Continual learning without forgetting of already learned information. These latter challenges have already been crucial topics in older literature, alas, seem to require a renaissance in modern deep learning literature. Initially, it may appear that they pose independent research questions, however, the thesis posits that the aspects are intertwined and require a joint perspective in machine learning based systems. In summary, the essential question is thus how to pick a suitable neural network architecture for a specific task, how to recognize which data inputs belong to this context, which ones originate from potential other tasks, and ultimately how to continuously include such identified novel data in neural network training over time without overwriting existing knowledge.
Thus, the central emphasis of this dissertation is to build on top of existing deep learning strengths, yet also acknowledge mentioned weaknesses, in an effort to establish a deeper understanding of interdependencies and synergies towards the development of unified solution mechanisms. For this purpose, the main portion of the thesis is in cumulative form. The respective publications can be grouped according to the three challenges outlined above. Correspondingly, chapter 1 is focused on choice and extendability of neural network architectures, analyzed in context of popular image classification tasks. An algorithm to automatically determine neural network layer width is introduced and is first contrasted with static architectures found in the literature. The importance of neural architecture design is then further showcased on a real-world application of defect detection in concrete bridges. Chapter 2 is comprised of the complementary ensuing questions of how to identify unknown concepts and subsequently incorporate them into continual learning. A joint central mechanism to distinguish unseen concepts from what is known in classification tasks, while enabling consecutive training without forgetting or revisiting older classes, is proposed. Once more, the role of the chosen neural network architecture is quantitatively reassessed. Finally, chapter 3 culminates in an overarching view, where developed parts are connected. Here, an extensive survey further serves the purpose to embed the gained insights in the broader literature landscape and emphasizes the importance of a common frame of thought. The ultimately presented approach thus reflects the overall thesis’ contribution to advance neural network based machine learning towards a unified solution that ties together choice of neural architecture with the ability to learn continually and the capability to automatically separate known from unknown data.
1970'li yıllarda özerk bir disiplin olarak kabul edilen çeviribilim, geçmişten günümüze edebiyat, psikoloji, sosyoloji, antropoloji ve tarih gibi pek çok sosyal bilim dalları ile etkileşime girdiği gibi başka alanları da etkilemiştir. Bu alanlardan biri de hiç kuşkusuz teknoloji olmuştur. 1980'li yıllarda teknolojinin gelişip değişmesi ile çeviribilim, yerelleştirme sektörünün ortaya çıkmasına aracılık etmiştir. 1990'lı yıllarda sektörün gelişmesinin sonucunda çevirmenler büyük projelerde bilgisayar destekli çeviri araçlarından daha fazla faydalanmaya başlamıştır. Sektörün ilk ortaya çıktığı zamanlardan bu yana yerelleştirmenin çeviribilimin alt dalı olup olmadığına ilişkin tartışmalar devam etmektedir. Bazı kuramcılar, çeviri ve yerelleştirmenin benzer nitelikler taşıdığını savunmasına karşın, bazı kuramcılar iki alanın da çok farklı sürece ve işleyişe sahip olduğunu dile getirmektedir. Bu çalışmada, farklı kuramcıları görüşlerinden yola çıkarak yerelleştirme sürecini oluşturan küreselleştirme, uluslararasılaştırma, yerelleştirme aşamaları tanımlanıp çeviribilim ve yerelleştirme arasındaki ilişki ele alınmıştır. Bu bağlamda, bu ilişkiden yola çıkılarak ürünlerin hedef kitleye ulaştırılması için çağdaş çeviri kuramcısı Anthony Pym tarafından kullanılan aktarım ve dağıtım kavramlarının yerelleştirme sürecinde hangi açılardan ele alındığı ve bu kavramların nasıl nitelendirildiği tartışılmıştır. Makalede aynı zamanda Pym'in yaklaşımına dayanarak yerelleştirme olgusunda ve sürecinde çevirinin işleyişi ve yeri de irdelenmiştir.
Locating a vocalizing animal can be useful in many fields of bioacoustics and behavioral research, and is often done in the wild, covering large areas. In zoos, however, the application of this method becomes particularly difficult, because, on the one hand, the animals are in a relatively small area and, on the other hand, reverberant environments and background noise complicate the analysis. Nevertheless, by localizing and analyzing animal sounds, valuable information on physiological state, sex, subspecies, reproductive state, social status, and animal welfare can be gathered. Therefore, we developed a sound localization software that is able to estimate the position of a vocalizing animal precisely, making it possible to assign the vocalization to the corresponding individual, even under difficult conditions. In this study, the accuracy and reliability of the software is tested under various conditions. Different vocalizations were played back through a loudspeaker and recorded with several microphones to verify the accuracy. In addition, tests were carried out under real conditions using the example of the giant otter enclosure at Dortmund Zoo, Germany. The results show that the software can estimate the correct position of a sound source with a high accuracy (median of the deviation 0.234 m). Consequently, this software could make an important contribution to basic research via position determination and the associated differentiation of individuals, and could be relevant in a long-term application for monitoring animal welfare in zoos.
Artificial Intelligence (AI) has the potential to greatly improve the delivery of healthcare and other services that advance population health and wellbeing. However, the use of AI in healthcare also brings potential risks that may cause unintended harm. To guide future developments in AI, the High-Level Expert Group on AI set up by the European Commission (EC), recently published ethics guidelines for what it terms “trustworthy” AI. These guidelines are aimed at a variety of stakeholders, especially guiding practitioners toward more ethical and more robust applications of AI. In line with efforts of the EC, AI ethics scholarship focuses increasingly on converting abstract principles into actionable recommendations. However, the interpretation, relevance, and implementation of trustworthy AI depend on the domain and the context in which the AI system is used. The main contribution of this paper is to demonstrate how to use the general AI HLEG trustworthy AI guidelines in practice in the healthcare domain. To this end, we present a best practice of assessing the use of machine learning as a supportive tool to recognize cardiac arrest in emergency calls. The AI system under assessment is currently in use in the city of Copenhagen in Denmark. The assessment is accomplished by an independent team composed of philosophers, policy makers, social scientists, technical, legal, and medical experts. By leveraging an interdisciplinary team, we aim to expose the complex trade-offs and the necessity for such thorough human review when tackling socio-technical applications of AI in healthcare. For the assessment, we use a process to assess trustworthy AI, called 1Z-Inspection® to identify specific challenges and potential ethical trade-offs when we consider AI in practice.
Cortical pyramidal neurons have a complex dendritic anatomy, whose function is an active research field. In particular, the segregation between its soma and the apical dendritic tree is believed to play an active role in processing feed-forward sensory information and top-down or feedback signals. In this work, we use a simple two-compartment model accounting for the nonlinear interactions between basal and apical input streams and show that standard unsupervised Hebbian learning rules in the basal compartment allow the neuron to align the feed-forward basal input with the top-down target signal received by the apical compartment. We show that this learning process, termed coincidence detection, is robust against strong distractions in the basal input space and demonstrate its effectiveness in a linear classification task.
Collaboration is an important 21st Century skill. Co-located (or face-to-face) collaboration (CC) analytics gained momentum with the advent of sensor technology. Most of these works have used the audio modality to detect the quality of CC. The CC quality can be detected from simple indicators of collaboration such as total speaking time or complex indicators like synchrony in the rise and fall of the average pitch. Most studies in the past focused on “how group members talk” (i.e., spectral, temporal features of audio like pitch) and not “what they talk”. The “what” of the conversations is more overt contrary to the “how” of the conversations. Very few studies studied “what” group members talk about, and these studies were lab based showing a representative overview of specific words as topic clusters instead of analysing the richness of the content of the conversations by understanding the linkage between these words. To overcome this, we made a starting step in this technical paper based on field trials to prototype a tool to move towards automatic collaboration analytics. We designed a technical setup to collect, process and visualize audio data automatically. The data collection took place while a board game was played among the university staff with pre-assigned roles to create awareness of the connection between learning analytics and learning design. We not only did a word-level analysis of the conversations, but also analysed the richness of these conversations by visualizing the strength of the linkage between these words and phrases interactively. In this visualization, we used a network graph to visualize turn taking exchange between different roles along with the word-level and phrase-level analysis. We also used centrality measures to understand the network graph further based on how much words have hold over the network of words and how influential are certain words. Finally, we found that this approach had certain limitations in terms of automation in speaker diarization (i.e., who spoke when) and text data pre-processing. Therefore, we concluded that even though the technical setup was partially automated, it is a way forward to understand the richness of the conversations between different roles and makes a significant step towards automatic collaboration analytics.
Device-to-device (D2D) communication is an innovative solution for improving wireless network performance to efficiently handle the ever-increasing mobile data traffic. Communication takes place directly between two devices that are in each other’s transmission range. So far, research has focused on the technical challenges of implementing this technology and assumes a user’s general willingness to participate as forwarder in this technology. However, this simplifying assumption is not realistic, as willingness to participate in D2D communication can vary depending on the user. In this work, we consider the scenario that a user can act as a forwarder for a receiver who is not directly or insufficiently reached by the base station and accordingly has no or poor Internet connection. We take a user-centric approach and investigate the willingness to provide an Internet connection as a forwarder. We are the first to investigate user preferences for D2D communication using a choice-based conjoint analysis. Our results, based on a representative sample of potential users (N=181), show that the social relationship between the potential forwarder and the receiver has the greatest impact on the potential forwarder’s decision to provide an Internet connection to the receiver, accepting sacrifices in terms of additional battery consumption and reduced own service performance. In a detailed segment analysis, we observe significant preference differences depending on smartphone usage behavior and user age. Taking the corresponding preferences into account when matching forwarders and receivers can further increase technology adoption.
Hintergrund. Ziel dieser Studie war es, zu bewerten, ob die Datenübertragung während peripherer endovaskulärer Eingriffe durch ein sprachgesteuertes, optisches Head-Mounted Display verwirklicht, und ob hierdurch der Arbeitsablauf der Intervention verbessert werden kann.
Methoden. Wir benutzten die Google Glass® Explorer Edition in Verbindung mit einer eigens entwickelten Glass App, um vorhandene Grafiken über die Datenbrille durch Sprachbefehle zugänglich zu machen. 40 Medizinstudenten im letzten Drittel des Medizinstudiums wurden in zwei Gruppen randomisiert.
Jeder Proband erhielt die Aufgabe eine PTA der A. femoralis superficialis an einem High-Fidelity-VR-Simulator (ANGIO-Mentor®, 3D Systems) durchzuführen. Während Gruppe A hierfür nötige Informationen über einen zusätzlich installierten Monitor erhielt, verwendete Gruppe B Google Glass®, um jeweilige Informationen durch zuvor definierte Sprachbefehle aufzurufen. Die objektive Bewertung der erbrachten Leistung erfolgte durch standardisierte Bewertungsbögen in dichotomer Nominalskalierung und durch die Messung der für die Aufgaben benötigten Zeit. Am Ende jeder Simulation erfolgte die subjektive Bewertung seitens der Probanden durch standardisierte Fragebögen mit 5-Level-Likert-Skalierung.
Ergebnisse. Eine maximale Punktzahl von 10 Punkten war erreichbar. Der in Gruppe A und Gruppe B gefundene Median lag bei 9 Punkten mit nicht signifikanten Abweichungen (p = 0,91). Die Gesamtdauer des Eingriffs betrug zwischen 12 und 14 Minuten. Gruppe B war unter Verwendung von Google Glass®, aufgrund technischer Schwierigkeiten mit der getesteten App, im Schnitt um 1:07 Minuten signifikant langsamer (p = 0,01). Dennoch konnte nachgewiesen werden, dass Google Glass® bei dem Transfer einfacher Informationen schneller oder zumindest gleichwertig gegenüber dem klassischen Monitoring war.
In diesem Kontext erachteten 92,5% der Probanden die Digitalisierung im klinischen Alltag als sinnvoll. 17 von 20 Teilnehmern (85%) empfanden die Handhabung von Google Glass® als einfach bis sehr einfach. Alle Teilnehmer waren der Ansicht, dass Augmented Reality bei peripheren endovaskulären Eingriffen im Katheterlabor nützlich sein könnte.
Schlussfolgerung. Google Glass® war dem klassischen Monitoring im Katheterlabor hinsichtlich der Gesamtinterventionszeit nur geringfügig unterlegen und behinderte den Arbeitsablauf während einer simulierten PTA der A. femoralis superficialis nicht. Unsere Studie offenbarte hierbei technische Schwierigkeiten bei der Genauigkeit der Spracherkennung und der Bildqualität von Google Glass®. Trotzdem konnten einzelne Aufgaben durch die Nutzung der Google Glass® signifikant schneller durchgeführt werden. Wir erwarten, dass nach Überwindung dieser technischen Probleme der Arbeitsablauf während endovaskulären Eingriffen mit einem optischen Head-Mounted Display verbessert werden kann.
1.Thedescriptionandanalysisofanimalbehavioroverlongperiodsoftimeisoneof the most important challenges in ecology. However, most of these studies are limited due to the time and cost required by human observers. The collection of data via video recordings allows observation periods to be extended. However, their evaluation by human observers is very time-consuming. Progress in automated evaluation, using suitable deep learning methods, seems to be a forward-looking approach to analyze even large amounts of video data in an adequate time frame.
2. In this study, we present a multistep convolutional neural network system for detecting three typical stances of African ungulates in zoo enclosures which works with high accuracy. An important aspect of our approach is the introduction of model averaging and postprocessing rules to make the system robust to outliers.
3. Our trained system achieves an in-domain classification accuracy of >0.92, which is improved to >0.96 by a postprocessing step. In addition, the whole system per- forms even well in an out-of-domain classification task with two unknown types, achieving an average accuracy of 0.93. We provide our system at https://github. com/Klimroth/Video-Action-Classifier-for-African-Ungulates-in-Zoos/tree/main/ mrcnn_based so that interested users can train their own models to classify im- ages and conduct behavioral studies of wildlife.
4. The use of a multistep convolutional neural network for fast and accurate clas- sification of wildlife behavior facilitates the evaluation of large amounts of image data in ecological studies and reduces the effort of manual analysis of images to a high degree. Our system also shows that postprocessing rules are a suitable way to make species-specific adjustments and substantially increase the accuracy of the description of single behavioral phases (number, duration). The results in the out-of-domain classification strongly suggest that our system is robust and achieves a high degree of accuracy even for new species, so that other settings (e.g., field studies) can be considered.
Veranstalter: Bernadette Biedermann, Universitätsmuseum, Universität Graz; Judith Blume, Universitätsbibliothek J.C. Senckenberg, Goethe-Universität Frankfurt am Main; Franziska Hormuth, Projekt „Digitales Netzwerk Sammlungen“, Berlin University Alliance / Humboldt-Universität zu Berlin
Datum, Ort: 22.04.2021–23.04.2021, digital
Co-design of a trustworthy AI system in healthcare: deep learning based skin lesion classifier
(2021)
This paper documents how an ethically aligned co-design methodology ensures trustworthiness in the early design phase of an artificial intelligence (AI) system component for healthcare. The system explains decisions made by deep learning networks analyzing images of skin lesions. The co-design of trustworthy AI developed here used a holistic approach rather than a static ethical checklist and required a multidisciplinary team of experts working with the AI designers and their managers. Ethical, legal, and technical issues potentially arising from the future use of the AI system were investigated. This paper is a first report on co-designing in the early design phase. Our results can also serve as guidance for other early-phase AI-similar tool developments.
Diese Arbeit beschäftigt sich mit linearen inversen Problemen, wie sie in einer Vielzahl an Anwendungen auftreten. Diese Probleme zeichnen sich dadurch aus, dass sie typischerweise schlecht gestellt sind, was in erster Linie die Stabilität betrifft. Selbst kleinste Messfehler haben enorme Konsequenzen für die Rekonstruktion der zu bestimmenden Größe.
Um eine robuste Rekonstruktion zu ermöglichen, muss das Problem regularisiert, dass heißt durch eine ganze Familie abgeänderter, stabiler Approximationen ersetzt werden. Die konkrete Wahl aus der Familie, die sogenannte Parameterwahlstrategie, stützt sich dann auf zusätzliche ad hoc Annahmen über den Messfehler. Typischerweise ist dies im deterministischen Fall die Kenntnis einer oberen Schranke an die Norm des Datenfehlers, oder im stochastischen Fall, die Kenntnis der Verteilung des Fehlers, beziehungsweise die Einschränkung auf eine bestimmte Klasse von Verteilungen, zumeist Gaußsche. In der vorliegenden Arbeit wird untersucht, wie sich diese Informationen unter der Annahme der Wiederholbarkeit der Messung gewinnen lassen. Die Daten werden dabei aus mehreren Messungen gemittelt, welche einer beliebigen, unbekannten Verteilung folgen, wobei die zur Lösung des Problems unweigerlich notwendige Fehlerschranke geschätzt wird. Auf Mittelwert und Schätzer wird dann ein klassisches Regularisierungsverfahren angewandt. Als Regularisierungen werden größtenteils Filter-basierte Verfahren behandelt, die sich auf die Spektralzerlegung des Problems stützen. Als Parameterwahlstrategien werden sowohl einfache a priori-Wahlen betrachtet, als auch das Diskrepanzprinzip als adaptives Verfahren. Es wird Konvergenz für unbekannte beliebige Fehlerverteilungen mit endlicher Varianz sowie für Weißes Rauschen (bezüglich allgemeiner Diskretisierungen) nachgewiesen. Schließlich wird noch die Konvergenz des Diskrepanzprinzips für ein stochastisches Gradientenverfahren gezeigt, als erste rigorose Analyse einer adaptiven Stoppregel für ein solches nicht Filter-basiertes Regularisierungsverfahren.
Point-based geometry representations have become widely used in numerous contexts, ranging from particle-based simulations, over stereo image matching, to depth sensing via light detection and ranging. Our application focus is on the reconstruction of curved line structures in noisy 3D point cloud data. Respective algorithms operating on such point clouds often rely on the notion of a local neighborhood. Regarding the latter, our approach employs multi-scale neighborhoods, for which weighted covariance measures of local points are determined. Curved line structures are reconstructed via vector field tracing, using a bidirectional piecewise streamline integration. We also introduce an automatic selection of optimal starting points via multi-scale geometric measures. The pipeline development and choice of parameters was driven by an extensive, automated initial analysis process on over a million prototype test cases. The behavior of our approach is controlled by several parameters — the majority being set automatically, leaving only three to be controlled by a user. In an extensive, automated final evaluation, we cover over one hundred thousand parameter sets, including 3D test geometries with varying curvature, sharp corners, intersections, data holes, and systematically applied varying types of noise. Further, we analyzed different choices for the point of reference in the co-variance computation; using a weighted mean performed best in most cases. In addition, we compared our method to current, publicly available line reconstruction frameworks. Up to thirty times faster execution times were achieved in some cases, at comparable error measures. Finally, we also demonstrate an exemplary application on four real-world 3D light detection and ranging datasets, extracting power line cables.
Diese Arbeit beschäftigt sich mit der theoriegeleiteten Entwicklung eines digitalen Werkzeugs namens MathCityMap (MCM) für das außerschulische Lehren und Lernen von Mathematik.
Den Ausgangspunkt des Projekts bilden die sogenannten Mathtrails. Dies sind Wanderpfade zum Entdecken mathematischer Sachverhalte an realen Objekten in der Umwelt. Eine didaktische, methodische sowie lernpsychologische Analyse konstatiert Mathtrails zahlreiche Potentiale für den Lernprozess wie beispielsweise die Möglichkeit, Primärerfahrungen zu sammeln, das Interesse am Fach Mathematik zu steigern sowie das Lernen aktiv und konstruktiv zu gestalten. Trotz der genannten Vorteile wird deutlich, dass die Vorbereitung und Umsetzung der mathematischen Wanderpfade mit einem immensen Aufwand verbunden sind. Eine weitere Herausforderung für Lernende liegt im offenen Charakter der Mathtrails, die in der Regel in autonomen Kleingruppen abgelaufen werden. Aus der Literatur ist bekannt, dass insbesondere für schwächere Lerner die Gefahr besteht, durch die Anforderungen einer selbstständigen Arbeitsweise überfordert zu werden.
Als Lösungsansatz für die zuvor genannten Probleme wird im Rahmen dieser Arbeit die Entwicklung eines digitalen Werkzeugs für Mathtrails erläutert. Die erste Forschungsfrage beschäftigt sich mit den theoretischen Anforderungen an solch ein Tool:
1. Welchen Anforderungen muss ein digitales Werkzeug genügen, um die Vorzüge der Mathtrails zu erhalten, deren Aufwand zu minimieren und die Gefahren zu kompensieren?
Unter Berücksichtigung der theoretischen Grundlagen digitaler Werkzeuge und des „Mobile Learnings“ werden zunächst Möglichkeiten identifiziert, den Vorbereitungsaufwand zu minimieren. Konkret erscheinen die automatische Datenverarbeitung, das digitale Zusammen-arbeiten sowie das Teilen und Wiederverwenden von digitalen Aufgaben und Trails als theoretisch zielführende Bestandteile von MCM. Weiterhin sollen zur Unterstützung der Lerner bei der eigenständigen Bearbeitung von Mathtrails didaktisch bewährte Konzepte – wie gestufte Hilfestellungen und Feedback – eingesetzt werden.
Vor dem Hintergrund der soeben formulierten Anforderungen bilden der Entwicklungsprozess sowie die Beschreibung des aktuellen Ist-Zustandes des MCM-Systems zentrale Bestand-teile dieser Arbeit. Das System setzt sich aus zwei Komponenten für jeweils unterschiedliche Zielgruppen zusammen: das MCM-Webportal zum Erstellen von Mathtrails und die MCM-App zum Ablaufen selbiger. Die Hauptziele von MCM können in der Minimierung des Vorbereitungsaufwands sowie der Kompensation einer Überforderungsgefahr gesehen werden.
In ersten Feldversuchen konnte MCM bereits in einem frühen Stadium erfolgreich mit Lernenden der Sekundarstufe I getestet werden. Gleichzeitig fiel jedoch auf, dass das implementierte Feedback-System Schwächen aufwies und von Lernenden zum systematischen Erraten von Lösungen genutzt werden konnte. In der Folge wurden Spielelemente (Gamification), denen nicht nur eine motivationssteigernde Wirkung nachgesagt wird, sondern auch das Potential das Verhalten zu beeinflussen, Bestandteil der MCM-App. Die zweite Forschungs-frage dieser Arbeit zielt auf die Auswirkungen der Gamification-Integration ab und lautet:
2. Welchen Einfluss haben Gamification-Elemente auf die Motivation sowie auf das Nutzungs-verhalten des digitalen Werkzeugs von Neuntklässlern bei der Bearbeitung eines Mathtrails?
Zur Beantwortung der zweiten Forschungsfrage wurde eine empirische Studie mit 16 Schulklassen (304 Schülerinnen und Schüler) der neunten Jahrgangsstufe im Sommer 2017 durch-geführt. Die Ergebnisse können wie folgt zusammengefasst werden: Die Implementierung einer Rangliste (Leaderboard) in die MCM-App führte zwar nicht zu einer höheren Motivation, jedoch spornte der Wettbewerb die Teilnehmer an, viele Aufgaben zu bearbeiten. Im Ver-gleich zu der Kontrollgruppe ohne Gamification-Elemente löste die Experimentalgruppe signifikant mehr Aufgaben, legte die doppelte Strecke zurück und nutzte das Feedbacks-System seltener aus, um Lösungen zu erraten. Die Studie konnte empirisch den gewünschten Einfluss von Spielelementen auf die Benutzung eines digitalen Werkzeugs für das außerschulische Lernen von Mathematik aufzeigen.
Die Evaluation der Ziele von MCM erfolgt indirekt über die Analyse der Verbreitung der Mathtrail-Idee ohne MCM und mit MCM. Die dritte Forschungsfrage lautet dementsprechend:
3. Welchen Beitrag hat das digitale Werkzeug zur Verbreitung der Mathtrail-Idee nach 4 Jahren Projektlaufzeit geleistet?
Zur Beantwortung der dritten Forschungsfrage werden wissenschaftliche Publikationen zu Mathtrails analysiert. Es wird insbesondere in Publikationen mit und ohne Stichwort „MathCityMap“ unterschieden, um eine Aussage über den Einfluss des MCM-Projekts auf den wissenschaftlichen Diskurs treffen zu können. Stand August 2020 enthält bereits jede dritte Mathtrail-Publikation einen Bezug zu MCM. Weiterhin wird ein Vergleich zu vorherigen, ähnlichen Bemühungen – gemeint sind Online-Mitmach-Projekte für Mathtrails – gezogen. So existierten im Zeitraum 2000 bis 2010 im anglo-amerikanischen Raum erste Webseiten für mathematische Wanderpfade. Diese boten zusammengenommen 131 Mathtrails an. Im Vergleich hierzu existieren bereits über 2.500 MCM-Mathtrails in 57 Ländern.
Sowohl die Publikationen als auch die Anzahl der erstellten Trails stellen erste Indizien dafür dar, dass mit MCM die Realisation eines theoretischen Konzepts für ein digitales Mathtrail-Werkzeug gelungen ist und die Idee der Mathtrails verbreitet werden konnte.
When requesting a web-based service, users often fail in setting the website’s privacy settings according to their self privacy preferences. Being overwhelmed by the choice of preferences, a lack of knowledge of related technologies or unawareness of the own privacy preferences are just some reasons why users tend to struggle. To address all these problems, privacy setting prediction tools are particularly well-suited. Such tools aim to lower the burden to set privacy preferences according to owners’ privacy preferences. To be in line with the increased demand for explainability and interpretability by regulatory obligations – such as the General Data Protection Regulation (GDPR) in Europe – in this paper an explainable model for default privacy setting prediction is introduced. Compared to the previous work we present an improved feature selection, increased interpretability of each step in model design and enhanced evaluation metrics to better identify weaknesses in the model’s design before it goes into production. As a result, we aim to provide an explainable and transparent tool for default privacy setting prediction which users easily understand and are therefore more likely to use.
Um den aktuellen Bildungsstand einer Gesellschaft abbilden zu können müssen Resultate von Bildungsprozessen, wie erworbenes Wissen oder ausgebildete Fähigkeiten, modelliert und gemessen werden (Leutner, Klieme, Fleischer & Kuper, 2013). Im Rahmen sogenannter Large-Scale-Assessments (LSAs) werden Kompetenzen in bestimmten Bereichen definiert und erfasst, die generell für die gesellschaftliche Teilhabe benötigen werden (bspw. Fraillon, Schulz & Ainley, 2013). Durch die fortschreitende Digitalisierung aller Lebens- und Arbeitsbereiche ist der kompetente Umgang mit Informations- und Kommunikationstechnologien (ICT) eine wichtige Voraussetzung für die erfolgreiche Teilhabe an unserer modernen Wissensgesellschaft. Die detaillierte Beschreibung solcher, auch als ICT-Skills bezeichneter Kompetenzen, und die Entwicklung von theoriebasierten Instrumenten zu deren Erfassung ist von großer Bedeutung, um mögliche sozial bedingte Disparitäten aufzudecken.
Im Rahmen der vorliegenden Arbeit werden Annahmen, Ergebnisse und Daten aus dem Projekt CavE-ICT, in dem verhaltensnahe simulationsbasierte Items zur Erfassung von ICT-Skills entwickelt wurden, aufgegriffen und weitergenutzt mit dem Ziel eine besonders effiziente und ökonomisch Messung von ICT-Skills im LSA-Kontext und darüber hinaus zu ermöglichen. Ein vielversprechender Ansatz durch den Testzeiten verkürzt und/oder die Messpräzision erhöht werden kann ist das computerisierte adaptive Testen (CAT; bspw. Frey, 2012). Beim adaptiven Testen orientiert sich die Auswahl der Items am Antwortverhalten der untersuchten Person, so dass durch die Berücksichtigung der individuellen Fähigkeit einer Person Items mit möglichst viel diagnostischer Information administriert werden können. Damit auch bei der Vorgabe unterschiedlicher Items in unterschiedlicher Reihenfolge Testleistungen von Personen miteinander verglichen werden können, stellen Modelle der Item-Response-Theorie (IRT; bspw. Hambleton & Swaminathan, 2010) die Basis der Anwendung von CAT dar.
Im Rahmen dieser Arbeit wurde untersucht, wie ICT-Skills auf Basis der Item-Response-Theorie und unter Einsatz computerisierter Messinstrumente erfasst werden können. Dabei setzten die empirischen Studien dieser Arbeit unterschiedliche Testformen um und an unterschiedlichen Punkten im Prozess der Testentwicklung an. Studie I setzt noch vor der Entwicklung von Items zur Messung von ICT-Skills an und zielt darauf ab Hinweise zum Umfang des zu erstellenden ICT-Itempools und zur Testlänge eines adaptiven Messinstruments bereitzustellen. Studie II baut direkt auf Studie I auf und nutzt die im Rahmen des Projekts CavE-ICT entwickelten und kalibrierten Items beziehungsweise ihre ermittelten Itemeigenschaften zur weiteren Erprobung verschiedener CAT-Algorithmen. Es werden Möglichkeiten aufgezeigt, wie multidimensionales adaptives Testen zur Messung von ICT-Skills gewinnbringend eingesetzt werden kann, und zudem eine differenzierte Messung auf Ebene der verschiedenen kognitiven Prozesse von ICT-Skills erlaubt. Dabei werden explizit Möglichkeiten exploriert Items die unterschiedliche kognitive Prozesse von ICT-Skills abbilden sequentiell geordnet und trotzdem adaptiv vorzulegen. Die durch Studie II erarbeiteten Erkenntnisse können insbesondere für die Erfassung von multidimensionalen Konstrukten oder facettierten Merkmalen in LSAs genutzt werden. Durch den Vergleich der Ergebnisse von Studie I und II ergeben sich zudem Implikationen für ein angemessenes Design von Simulationsstudien die insbesondere noch vor der eigentlichen Test- beziehungsweise Itementwicklung ansetzen. In Studie III werden lineare Kurztests zur Messung von ICT-Skills zusammengestellt. Durch die gezielte Auswahl geeigneter ICT-Items soll bei möglichst geringer Testzeit zugleich eine hohe Messgenauigkeit und Zuverlässigkeit realisiert werden. Die in Studie III manuell und automatisiert computerbasiert zusammengestellten Tests werden hinsichtlich des Einsatzes sowohl auf Populationsebene, im Sinne einschlägiger LSAs, als auch darüber hinaus für gruppen- und individualdiagnostische Zwecke evaluiert und Empfehlungen für den Kurztesteinsatz abgeleitet.
This thesis explores a variety of methods of text quantification applicable in the field of educational text technology. Besides the cohort of existing linguistic, lexical, syntactic, and semantic text quantification methods, additional methods based on Bidirectional Encoder Representations from Transformers (BERT) are introduced and analysed. The model, developed in this thesis, is tested on a multilingual data composed of task descriptions used in Test of Understanding in College Economics (TUCE). Quantitative features extracted from raw textual data are analysed using an array of evaluation methods with the goal of finding the best predictors of the target variable - the rate of correct student responses in TUCE.
It's been ten years since open data first broke onto the global stage. Over the past decade, thousands of programmes and projects around the world have worked to open data and use it to address a myriad of social and economic challenges. Meanwhile, issues related to data rights and privacy have moved to the centre of public and political discourse. As the open data movement enters a new phase in its evolution, shifting to target real-world problems and embed open data thinking into other existing or emerging communities of practice, big questions still remain. How will open data initiatives respond to new concerns about privacy, inclusion, and artificial intelligence? And what can we learn from the last decade in order to deliver impact where it is most needed? The State of Open Data brings together over 60 authors from around the world to address these questions and to take stock of the real progress made to date across sectors and around the world, uncovering the issues that will shape the future of open data in the years to come.
High-energy physics experiments aim to deepen our understanding of the fundamental structure of matter and the governing forces. One of the most challenging aspects of the design of new experiments is data management and event selection. The search for increasingly rare and intricate physics events asks for high-statistics measurements and sophisticated event analysis. With progressively complex event signatures, traditional hardware-based trigger systems reach the limits of realizable latency and complexity. The Compressed Baryonic Matter experiment (CBM) employs a novel approach for data readout and event selection to address these challenges. Self-triggered, free-streaming detectors push all data to a central compute cluster, called First-level Event Selector (FLES), for software-based event analysis and selection. While this concept solves many issues present in classical architectures, it also sets new challenges for the design of the detector readout systems and online event selection.
This thesis presents an efficient solution to the data management challenges presented by self-triggered, free-streaming particle detectors. The FLES must receive asynchronously streamed data from a heterogeneous detector setup at rates of up to 1 TB/s. The real-time processing environment implies that all components have to deliver high performance and reliability to record as much valuable data as possible. The thesis introduces a time-based data model to partition the input streams into containers of fixed length in experiment time for efficient data management. These containers provide all necessary metadata to enable generic, detector-subsystem-agnostic data distribution across the entire cluster. An analysis shows that the introduced data overhead is well below 1 % for a wide range of system parameters.
Furthermore, a concept and the implementation of a detector data input interface for the CBM FLES, optimized for resource-efficient data transport, are presented. The central element of the architecture is an FPGA-based PCIe extension card for the FLES entry nodes. The hardware designs developed in the thesis enable interfacing with a diverse set of detector systems. A custom, high-throughput DMA design structures data in a way that enables low-overhead access and efficient software processing. The ability to share the host DMA buffers with other devices, such as an InfiniBand HCA, allows for true zero-copy data distribution between the cluster nodes. The discussed FLES input interface is fully implemented and has already proven its reliability in production operation in various physics experiments.
In order to address security and privacy problems in practice, it is very important to have a solid elicitation of requirements, before trying to address the problem. In this thesis, specific challenges of the areas of social engineering, security management and privacy enhancing technologies are analyzed:
Social Engineering: An overview of existing tools usable for social engineering is provided and defenses against social engineering are analyzed. Serious games are proposed as a more pleasant way to raise employees’ awareness and to train them.
Security Management: Specific requirements for small and medium sized energy providers are analyzed and a set of tools to support them in assessing security risks and improving their security is proposed. Larger enterprises are supported by a method to collect security key performance indicators for different subsidiaries and with a risk assessment method for apps on mobile devices. Furthermore, a method to select a secure cloud provider – the currently most popular form of outsourcing – is provided.
Privacy Enhancing Technologies: Relevant factors for the users’ adoption of privacy enhancing technologies are identified and economic incentives and hindrances for companies are discussed. Privacy by design is applied to integrate privacy into the use cases e-commerce and internet of things.
Elderly people still play a minor role in research on information needs and usage patterns of Internet users. Online research and advocacy groups look optimistically at the (economic and social) potential of the active and technology–skilled elderly; other approaches dealing with the social appropriation of technology see obstacles and stress the dangers of an increasing digital divide between generations. Our objective is to refer to taken for granted normative assumptions of the digital divide discourse, highlighting different requirements for the appropriation of the Internet. Using the concept of technological generations we look at formal and informal learning of young and elderly people in the German context. We use survey material and field impressions we gained in various technology related studies. The results show that the "two worlds apart" assumption (young vs. elderly people) is too simplistic. Factors like gender, education and socio–economic status still play an important role for acceptance and diffusion of a technology. The diffusion rate among the elderly is increasing, but will continue to lag behind the figures of the young users. Cultural preparations and easy access modes are essential for the elderly, who could make use of latecomer advantages. Informal learning and peer group support will be crucial for the diffusion of the Internet among the elderly. In our conclusions we look at the specific social status of the elderly cohort, which makes a comparison with other social groups very difficult.
Begriffe sind häufig nicht eindeutig. Eine „Bank“ kann ein Finanzinstitut oder eine Sitzgelegenheit sein und die Stadt Frankfurt existiert mehr als einmal. Dennoch können sie in vielen Fällen problemlos von Menschen unterschieden werden. Computer sind noch nicht in der Lage, diese Leistung mit vergleichbarer Genauigkeit zu erfüllen.
Der in dieser Arbeit vorgestellte Ansatz baut auf dem für das Deutsche bereits gute Ergebnisse erzielenden fastSense auf und verwendet ein neuronales Netz, um Namen und Begriffe in englischen Texten mit Hilfe der Wikipedia zu disambiguieren. Dabei konnte eine Genauigkeit von bis zu 89,5% auf Testdaten erreicht werden.
Mit dem entwickelten Python-Modul kann das trainierte Modell in bestehende Anwendungen eingebunden werden. Die im Modul enthaltenen Programme ermöglichen es, neue Modelle zu trainieren und zu testen.
Ein großer Tag für das Institut für Informatik der Goethe-Universität: Es feiert sein 40-jähriges Jubiläum mit einem Festakt am Freitag, den 13. Oktober, ab 14 Uhr im Casino auf dem Campus Westend. Die Universität hat zusammen mit dem Alumni- und Förderverein hochkarätige Gastredner eingeladen, die ein Bild von der Bedeutung der Informatik und ihrer Forschung zeichnen werden.
We investigate privacy concerns and the privacy behavior of users of the AR smartphone game Pokémon Go. Pokémon Go accesses several functionalities of the smartphone and, in turn, collects a plethora of data of its users. For assessing the privacy concerns, we conduct an online study in Germany with 683 users of the game. The results indicate that the majority of the active players are concerned about the privacy practices of companies. This result hints towards the existence of a cognitive dissonance, i.e. the privacy paradox. Since this result is common in the privacy literature, we complement the first study with a second one with 199 users, which aims to assess the behavior of users with regard to which measures they undertake for protecting their privacy. The results are highly mixed and dependent on the measure, i.e. relatively many participants use privacy-preserving measures when interacting with their smartphone. This implies that many users know about risks and might take actions to protect their privacy, but deliberately trade-off their information privacy for the utility generated by playing the game.
Privacy and its protection is an important part of the culture in the USA and Europe. Literature in this field lacks empirical data from Japan. Thus, it is difficult– especially for foreign researchers – to understand the situation in Japan. To get a deeper understanding we examined the perception of a topic that is closely related to privacy: the perceived benefits of sharing data and the willingness to share in respect to the benefits for oneself, others and companies. We found a significant impact of the gender to each of the six analysed constructs.
This paper provides an assessment framework for privacy policies of Internet of Things Services which is based on particular GDPR requirements. The objective of the framework is to serve as supportive tool for users to take privacy-related informed decisions. For example when buying a new fitness tracker, users could compare different models in respect to privacy friendliness or more particular aspects of the framework such as if data is given to a third party. The framework consists of 16 parameters with one to four yes-or-no-questions each and allows the users to bring in their own weights for the different parameters. We assessed 110 devices which had 94 different policies. Furthermore, we did a legal assessment for the parameters to deal with the case that there is no statement at all regarding a certain parameter. The results of this comparative study show that most of the examined privacy policies of IoT devices/services are insufficient to address particular GDPR requirements and beyond. We also found a correlation between the length of the policy and the privacy transparency score, respectively.
In the upcoming years, the internet of things (IoT)will enrich daily life. The combination of artificial intelligence(AI) and highly interoperable systems will bring context-sensitive multi-domain services to reality. This paper describesa concept for an AI-based smart living platform with open-HAB, a smart home middleware, and Web of Things (WoT) askey components of our approach. The platform concept con-siders different stakeholders, i.e. the housing industry, serviceproviders, and tenants. These activities are part of the Fore-Sight project, an AI-driven, context-sensitive smart living plat-form.
Background: In pain research and clinics, it is common practice to subgroup subjects according to shared pain characteristics. This is often achieved by computer‐aided clustering. In response to a recent EU recommendation that computer‐aided decision making should be transparent, we propose an approach that uses machine learning to provide (1) an understandable interpretation of a cluster structure to (2) enable a transparent decision process about why a person concerned is placed in a particular cluster.
Methods: Comprehensibility was achieved by transforming the interpretation problem into a classification problem: A sub‐symbolic algorithm was used to estimate the importance of each pain measure for cluster assignment, followed by an item categorization technique to select the relevant variables. Subsequently, a symbolic algorithm as explainable artificial intelligence (XAI) provided understandable rules of cluster assignment. The approach was tested using 100‐fold cross‐validation.
Results: The importance of the variables of the data set (6 pain‐related characteristics of 82 healthy subjects) changed with the clustering scenarios. The highest median accuracy was achieved by sub‐symbolic classifiers. A generalized post‐hoc interpretation of clustering strategies of the model led to a loss of median accuracy. XAI models were able to interpret the cluster structure almost as correctly, but with a slight loss of accuracy.
Conclusions: Assessing the variables importance in clustering is important for understanding any cluster structure. XAI models are able to provide a human‐understandable interpretation of the cluster structure. Model selection must be adapted individually to the clustering problem. The advantage of comprehensibility comes at an expense of accuracy.
We study online secretary problems with returns in combinatorial packing domains with n candidates that arrive sequentially over time in random order. The goal is to determine a feasible packing of candidates of maximum total value. In the first variant, each candidate arrives exactly twice. All 2n arrivals occur in random order. We propose a simple 0.5‐competitive algorithm. For the online bipartite matching problem, we obtain an algorithm with ratio at least 0.5721 − o(1), and an algorithm with ratio at least 0.5459 for all n ≥ 1. We extend all algorithms and ratios to k ≥ 2 arrivals per candidate. In the second variant, there is a pool of undecided candidates. In each round, a random candidate from the pool arrives. Upon arrival a candidate can be either decided (accept/reject) or postponed. We focus on minimizing the expected number of postponements when computing an optimal solution. An expected number of Θ(n log n) is always sufficient. For bipartite matching, we can show a tight bound of O(r log n), where r is the size of the optimum matching. For matroids, we can improve this further to a tight bound of O(r′ log(n/r′)), where r′ is the minimum rank of the matroid and the dual matroid.