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Freitag, 22. Mai 2020, 11:30 Uhr
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Ort: Raum 348 (Gebäude 50.34)
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(Keine Vorträge)
Freitag, 29. Mai 2020, 11:30 Uhr
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Ort: Raum 010 (Gebäude 50.34)
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Freitag, 29. Mai 2020, 11:30 Uhr
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| Vortragende(r) | Hannes Kuchelmeister |
|---|---|
| Titel | Decision Support for Group-Based Configuration using Recommender Systems |
| Vortragstyp | Bachelorarbeit |
| Betreuer(in) | Robert Heinrich |
| Vortragsmodus | |
| Kurzfassung | A group of people with diferent personal preferences wants to fnd a solution to a problem with high variability. Making decisions in the group comes with problems as a lack of communication leads to worse decision outcomes. Group dynamics and biases can lead to suboptimal decisions. Generally group decisions are complex and often the process that yields the
decision result is unstructured, thereby not providing any reproducibility of the success. Groups have different power structures and usually individuals have diferent interests. Moreover finding solutions is a rather complex task and group decisions can sufer intransparency. To support groups in their decision making product confguration can be used. It allows to accurately map constraints and dependencies in complex problems and to map the solution space. Using a group recommender a group is supported in their confguration decisions. The goal is to show that these approaches can help a group with the confguration task presented by the usage of a configurator and to better process individual preferences than a human can. The benefts of this approach are, that the need for a group to communicate directly is reduced. Each user gives their own preferences and the group will get a recommendation based on that. This allows to reduce problems arising in groups decisions like lack of communication and bias in groups. Additionally this shows the viability of combining group recommendations and configuration approaches. |
| Vortragende(r) | Larissa Schmid |
|---|---|
| Titel | Modeling and Simulation of Message-Driven Self-Adaptive Systems |
| Vortragstyp | Masterarbeit |
| Betreuer(in) | Jörg Henß |
| Vortragsmodus | |
| Kurzfassung | Dynamic systems that reconfigure themselves use message queues as a common method to achieve decoupling between senders and receivers. Predicting the quality of systems at design time is crucial as changes in later phases of development get way more costly. At the moment, there is no method to represent message queues on an architectural level and predict their quality impact on systems. This work proposes a meta-model for enabling such representation and a simulation interface between a simulation of a component-based architecture description language and a messaging simulation. The interface is implemented for the Palladio simulator SimuLizar and an AMQP simulation. This enables architectural representation of messaging and predicting quality attributes of message-driven self-adaptive systems. The evaluation with a case study shows the applicability of the approach and its prediction accuracy for Point-To-Point communication. |
Freitag, 5. Juni 2020, 11:30 Uhr
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Ort: Raum 348 (Gebäude 50.34)
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| Vortragende(r) | Moritz Renftle |
|---|---|
| Titel | Assessing Human Understanding of Machine Learning Models |
| Vortragstyp | Masterarbeit |
| Betreuer(in) | Holger Trittenbach |
| Vortragsmodus | |
| Kurzfassung | To deploy an ML model in practice, a stakeholder needs to understand the behaviour and implications of this model. To help stakeholders develop this understanding, researchers propose a variety of technical approaches, so called eXplainable Artificial Intelligence (XAI). Current XAI approaches follow very task- or model-specific objectives. There is currently no consensus on a generic method to evaluate most of these technical solutions. This complicates comparing different XAI approaches and choosing an appropriate solution in practice. To address this problem, we formally define two generic experiments to measure human understanding of ML models. From these definitions we derive two technical strategies to improve understanding, namely (1) training a surrogate model and (2) translating inputs and outputs to effectively perceivable features. We think that most existing XAI approaches only focus on the first strategy. Moreover, we show that established methods to train ML models can also help stakeholders to better understand ML models. In particular, they help to mitigate cognitive biases. In a case study, we demonstrate that our experiments are practically feasible and useful. We suggest that future research on XAI should use our experiments as a template to design and evaluate technical solutions that actually improve human understanding. |
| Vortragende(r) | Alan Mazankiewicz |
|---|---|
| Titel | Incremental Real-Time Personalization in Human Activity Recognition Using Domain Adaptive Batch Normalization |
| Vortragstyp | Masterarbeit |
| Betreuer(in) | Klemens Böhm |
| Vortragsmodus | |
| Kurzfassung | Human Activity Recognition (HAR) from accelerometers is a fundamental problem in ubiquitous computing. Machine learning based recognition models often perform poorly when applied to new users that were not part of the training data. Previous work has addressed this challenge by personalizing general recognition models to the motion pattern of a new user in a static batch setting. The more challenging online setting has received less attention. No samples from the target user are available in advance, but they arrive sequentially. Additionally, the user's motion pattern may change over time. Thus, adapting to new and forgetting old information must be traded off. Finally, the target user should not have to do any work to use the recognition system by labeling activities. Our work addresses this challenges by proposing an unsupervised online domain adaptation algorithm. It works by aligning the feature distribution of all the subjects, sources and target, within deep neural network layers. |
Freitag, 5. Juni 2020, 14:00 Uhr
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(Keine Vorträge)