@inproceedings{35426, author = {{Richter, Cedric and Haltermann, Jan Frederik and Jakobs, Marie-Christine and Pauck, Felix and Schott, Stefan and Wehrheim, Heike}}, booktitle = {{37th IEEE/ACM International Conference on Automated Software Engineering}}, publisher = {{ACM}}, title = {{{Are Neural Bug Detectors Comparable to Software Developers on Variable Misuse Bugs?}}}, doi = {{10.1145/3551349.3561156}}, year = {{2023}}, } @inproceedings{35427, author = {{Pauck, Felix}}, booktitle = {{37th IEEE/ACM International Conference on Automated Software Engineering}}, publisher = {{ACM}}, title = {{{Scaling Arbitrary Android App Analyses}}}, doi = {{10.1145/3551349.3561339}}, year = {{2023}}, } @inproceedings{44194, author = {{Ahmed, Qazi Arbab and Awais, Muhammad and Platzner, Marco}}, booktitle = {{The 24th International Symposium on Quality Electronic Design (ISQED'23), San Francisco, Califorina USA}}, location = {{San Fransico CA 94023-0607, USA}}, title = {{{MAAS: Hiding Trojans in Approximate Circuits}}}, year = {{2023}}, } @phdthesis{43108, author = {{Pauck, Felix}}, publisher = {{Paderborn University}}, title = {{{Cooperative Android App Analysis}}}, doi = {{10.17619/UNIPB/1-1698}}, year = {{2023}}, } @phdthesis{44323, abstract = {{Reading between the lines has so far been reserved for humans. The present dissertation addresses this research gap using machine learning methods. Implicit expressions are not comprehensible by computers and cannot be localized in the text. However, many texts arise on interpersonal topics that, unlike commercial evaluation texts, often imply information only by means of longer phrases. Examples are the kindness and the attentiveness of a doctor, which are only paraphrased (“he didn’t even look me in the eye”). The analysis of such data, especially the identification and localization of implicit statements, is a research gap (1). This work uses so-called Aspect-based Sentiment Analysis as a method for this purpose. It remains open how the aspect categories to be extracted can be discovered and thematically delineated based on the data (2). Furthermore, it is not yet explored how a collection of tools should look like, with which implicit phrases can be identified and thus made explicit (3). Last, it is an open question how to correlate the identified phrases from the text data with other data, including the investigation of the relationship between quantitative scores (e.g., school grades) and the thematically related text (4). Based on these research gaps, the research question is posed as follows: Using text mining methods, how can implicit rating content be properly interpreted and thus made explicit before it is automatically categorized and quantified? The uniqueness of this dissertation is based on the automated recognition of implicit linguistic statements alongside explicit statements. These are identified in unstructured text data so that features expressed only in the text can later be compared across data sources, even though they were not included in rating categories such as stars or school grades. German-language physician ratings from websites in three countries serve as the sample domain. The solution approach consists of data creation, a pipeline for text processing and analyses based on this. In the data creation, aspect classes are identified and delineated across platforms and marked in text data. This results in six datasets with over 70,000 annotated sentences and detailed guidelines. The models that were created based on the training data extract and categorize the aspects. In addition, the sentiment polarity and the evaluation weight, i. e., the importance of each phrase, are determined. The models, which are combined in a pipeline, are used in a prototype in the form of a web application. The analyses built on the pipeline quantify the rating contents by linking the obtained information with further data, thus allowing new insights. As a result, a toolbox is provided to identify quantifiable rating content and categories using text mining for a sample domain. This is used to evaluate the approach, which in principle can also be adapted to any other domain.}}, author = {{Kersting, Joschka}}, pages = {{208}}, publisher = {{Universität der Bundeswehr München }}, title = {{{Identifizierung quantifizierbarer Bewertungsinhalte und -kategorien mittels Text Mining}}}, year = {{2023}}, } @inbook{45888, author = {{Wehrheim, Heike and Platzner, Marco and Bodden, Eric and Schubert, Philipp and Pauck, Felix and Jakobs, Marie-Christine}}, booktitle = {{On-The-Fly Computing -- Individualized IT-services in dynamic markets}}, editor = {{Haake, Claus-Jochen and Meyer auf der Heide, Friedhelm and Platzner, Marco and Wachsmuth, Henning and Wehrheim, Heike}}, pages = {{125--144}}, publisher = {{Heinz Nixdorf Institut, Universität Paderborn}}, title = {{{Verifying Software and Reconfigurable Hardware Services}}}, doi = {{10.5281/zenodo.8068583}}, volume = {{412}}, year = {{2023}}, } @inbook{45882, author = {{Bäumer, Frederik Simon and Chen, Wei-Fan and Geierhos, Michaela and Kersting, Joschka and Wachsmuth, Henning}}, booktitle = {{On-The-Fly Computing -- Individualized IT-services in dynamic markets}}, editor = {{Haake, Claus-Jochen and Meyer auf der Heide, Friedhelm and Platzner, Marco and Wachsmuth, Henning and Wehrheim, Heike}}, pages = {{65--84}}, publisher = {{Heinz Nixdorf Institut, Universität Paderborn}}, title = {{{Dialogue-based Requirement Compensation and Style-adjusted Data-to-text Generation}}}, doi = {{10.5281/zenodo.8068456}}, volume = {{412}}, year = {{2023}}, } @inbook{45884, author = {{Hanselle, Jonas Manuel and Hüllermeier, Eyke and Mohr, Felix and Ngonga Ngomo, Axel-Cyrille and Sherif, Mohamed and Tornede, Alexander and Wever, Marcel Dominik}}, booktitle = {{On-The-Fly Computing -- Individualized IT-services in dynamic markets}}, editor = {{Haake, Claus-Jochen and Meyer auf der Heide, Friedhelm and Platzner, Marco and Wachsmuth, Henning and Wehrheim, Heike}}, pages = {{85--104}}, publisher = {{Heinz Nixdorf Institut, Universität Paderborn}}, title = {{{Configuration and Evaluation}}}, doi = {{10.5281/zenodo.8068466}}, volume = {{412}}, year = {{2023}}, } @inbook{45886, author = {{Wehrheim, Heike and Hüllermeier, Eyke and Becker, Steffen and Becker, Matthias and Richter, Cedric and Sharma, Arnab}}, booktitle = {{On-The-Fly Computing -- Individualized IT-services in dynamic markets}}, editor = {{Haake, Claus-Jochen and Meyer auf der Heide, Friedhelm and Platzner, Marco and Wachsmuth, Henning and Wehrheim, Heike}}, pages = {{105--123}}, publisher = {{Heinz Nixdorf Institut, Universität Paderborn}}, title = {{{Composition Analysis in Unknown Contexts}}}, doi = {{10.5281/zenodo.8068510}}, volume = {{412}}, year = {{2023}}, } @inbook{46205, abstract = {{We present a concept for quantifying evaluative phrases to later compare rating texts numerically instead of just relying on stars or grades. We achievethis by combining deep learning models in an aspect-based sentiment analysis pipeline along with sentiment weighting, polarity, and correlation analyses that combine deep learning results with metadata. The results provide new insights for the medical field. Our application domain, physician reviews, shows that there are millions of review texts on the Internet that cannot yet be comprehensively analyzed because previous studies have focused on explicit aspects from other domains (e.g., products). We identify, extract, and classify implicit and explicit aspect phrases equally from German-language review texts. To do so, we annotated aspect phrases representing reviews on numerous aspects of a physician, medical practice, or practice staff. We apply the best performing transformer model, XLM-RoBERTa, to a large physician review dataset and correlate the results with existing metadata. As a result, we can show different correlations between the sentiment polarity of certain aspect classes (e.g., friendliness, practice equipment) and physicians’ professions (e.g., surgeon, ophthalmologist). As a result, we have individual numerical scores that contain a variety of information based on deep learning algorithms that extract textual (evaluative) information and metadata from the Web.}}, author = {{Kersting, Joschka and Geierhos, Michaela}}, booktitle = {{Data Management Technologies and Applications}}, editor = {{Cuzzocrea, Alfredo and Gusikhin, Oleg and Hammoudi, Slimane and Quix, Christoph}}, isbn = {{9783031378898}}, issn = {{1865-0929}}, pages = {{45--65}}, publisher = {{Springer Nature Switzerland}}, title = {{{Towards Comparable Ratings: Quantifying Evaluative Phrases in Physician Reviews}}}, doi = {{10.1007/978-3-031-37890-4_3}}, volume = {{1860}}, year = {{2023}}, }