@inproceedings{16724, author = {{Sharma, Arnab and Wehrheim, Heike}}, booktitle = {{Proceedings of the ACM SIGSOFT International Symposium on Software Testing and Analysis (ISSTA).}}, publisher = {{ACM}}, title = {{{Higher Income, Larger Loan? Monotonicity Testing of Machine Learning Models}}}, year = {{2020}}, } @article{16725, author = {{Richter, Cedric and Hüllermeier, Eyke and Jakobs, Marie-Christine and Wehrheim, Heike}}, journal = {{Journal of Automated Software Engineering}}, publisher = {{Springer}}, title = {{{Algorithm Selection for Software Validation Based on Graph Kernels}}}, year = {{2020}}, } @inproceedings{15580, abstract = {{This paper deals with aspect phrase extraction and classification in sentiment analysis. We summarize current approaches and datasets from the domain of aspect-based sentiment analysis. This domain detects sentiments expressed for individual aspects in unstructured text data. So far, mainly commercial user reviews for products or services such as restaurants were investigated. We here present our dataset consisting of German physician reviews, a sensitive and linguistically complex field. Furthermore, we describe the annotation process of a dataset for supervised learning with neural networks. Moreover, we introduce our model for extracting and classifying aspect phrases in one step, which obtains an F1-score of 80%. By applying it to a more complex domain, our approach and results outperform previous approaches.}}, author = {{Kersting, Joschka and Geierhos, Michaela}}, booktitle = {{Proceedings of the 12th International Conference on Agents and Artificial Intelligence (ICAART 2020) -- Special Session on Natural Language Processing in Artificial Intelligence (NLPinAI 2020)}}, keywords = {{Deep Learning, Natural Language Processing, Aspect-based Sentiment Analysis}}, location = {{Valetta, Malta}}, pages = {{391----400}}, publisher = {{SCITEPRESS}}, title = {{{Aspect Phrase Extraction in Sentiment Analysis with Deep Learning}}}, year = {{2020}}, } @inproceedings{15582, abstract = {{When it comes to increased digitization in the health care domain, privacy is a relevant topic nowadays. This relates to patient data, electronic health records or physician reviews published online, for instance. There exist different approaches to the protection of individuals’ privacy, which focus on the anonymization and masking of personal information subsequent to their mining. In the medical domain in particular, measures to protect the privacy of patients are of high importance due to the amount of sensitive data that is involved (e.g. age, gender, illnesses, medication). While privacy breaches in structured data can be detected more easily, disclosure in written texts is more difficult to find automatically due to the unstructured nature of natural language. Therefore, we take a detailed look at existing research on areas related to privacy protection. Likewise, we review approaches to the automatic detection of privacy disclosure in different types of medical data. We provide a survey of several studies concerned with privacy breaches in the medical domain with a focus on Physician Review Websites (PRWs). Finally, we briefly develop implications and directions for further research.}}, author = {{Buff, Bianca and Kersting, Joschka and Geierhos, Michaela}}, booktitle = {{Proceedings of the 9th International Conference on Pattern Recognition Applications and Methods (ICPRAM 2020)}}, keywords = {{Identity Disclosure, Privacy Protection, Physician Review Website, De-Anonymization, Medical Domain}}, location = {{Valetta, Malta}}, pages = {{630----637}}, publisher = {{SCITEPRESS}}, title = {{{Detection of Privacy Disclosure in the Medical Domain: A Survey}}}, year = {{2020}}, } @inproceedings{15629, abstract = {{In multi-label classification (MLC), each instance is associated with a set of class labels, in contrast to standard classification where an instance is assigned a single label. Binary relevance (BR) learning, which reduces a multi-label to a set of binary classification problems, one per label, is arguably the most straight-forward approach to MLC. In spite of its simplicity, BR proved to be competitive to more sophisticated MLC methods, and still achieves state-of-the-art performance for many loss functions. Somewhat surprisingly, the optimal choice of the base learner for tackling the binary classification problems has received very little attention so far. Taking advantage of the label independence assumption inherent to BR, we propose a label-wise base learner selection method optimizing label-wise macro averaged performance measures. In an extensive experimental evaluation, we find that or approach, called LiBRe, can significantly improve generalization performance.}}, author = {{Wever, Marcel Dominik and Tornede, Alexander and Mohr, Felix and Hüllermeier, Eyke}}, location = {{Konstanz, Germany}}, publisher = {{Springer}}, title = {{{LiBRe: Label-Wise Selection of Base Learners in Binary Relevance for Multi-Label Classification}}}, year = {{2020}}, } @inproceedings{15635, author = {{Kersting, Joschka and Geierhos, Michaela}}, booktitle = {{Proceedings of the 33rd International Florida Artificial Intelligence Research Symposium (FLAIRS) Conference}}, location = {{North Miami Beach, FL, USA}}, pages = {{282----285}}, publisher = {{AAAI}}, title = {{{Neural Learning for Aspect Phrase Extraction and Classification in Sentiment Analysis}}}, year = {{2020}}, } @article{15025, abstract = {{In software engineering, the imprecise requirements of a user are transformed to a formal requirements specification during the requirements elicitation process. This process is usually guided by requirements engineers interviewing the user. We want to partially automate this first step of the software engineering process in order to enable users to specify a desired software system on their own. With our approach, users are only asked to provide exemplary behavioral descriptions. The problem of synthesizing a requirements specification from examples can partially be reduced to the problem of grammatical inference, to which we apply an active coevolutionary learning approach. However, this approach would usually require many feedback queries to be sent to the user. In this work, we extend and generalize our active learning approach to receive knowledge from multiple oracles, also known as proactive learning. The ‘user oracle’ represents input received from the user and the ‘knowledge oracle’ represents available, formalized domain knowledge. We call our two-oracle approach the ‘first apply knowledge then query’ (FAKT/Q) algorithm. We compare FAKT/Q to the active learning approach and provide an extensive benchmark evaluation. As result we find that the number of required user queries is reduced and the inference process is sped up significantly. Finally, with so-called On-The-Fly Markets, we present a motivation and an application of our approach where such knowledge is available.}}, author = {{Wever, Marcel Dominik and van Rooijen, Lorijn and Hamann, Heiko}}, journal = {{Evolutionary Computation}}, number = {{2}}, pages = {{165–193}}, publisher = {{MIT Press Journals}}, title = {{{Multi-Oracle Coevolutionary Learning of Requirements Specifications from Examples in On-The-Fly Markets}}}, doi = {{10.1162/evco_a_00266}}, volume = {{28}}, year = {{2020}}, } @inproceedings{15256, abstract = {{This paper deals with online customer reviews of local multi-service providers. While many studies investigate product reviews and online labour markets with service providers delivering intangible products “over the wire”, we focus on websites where providers offer multiple distinct services that can be booked, paid and reviewed online but are performed locally offline. This type of service providers has so far been neglected in the literature. This paper analyses reviews and applies sentiment analysis. It aims to gain new insights into local multi-service providers’ performance. There is a broad literature range presented with regard to the topics addressed. The results show, among other things, that providers with good ratings continue to perform well over time. We find that many positive reviews seem to encourage sales. On average, quantitative star ratings and qualitative ratings in the form of review texts match. Further results are also achieved in this study.}}, author = {{Kersting, Joschka and Geierhos, Michaela}}, booktitle = {{Proceedings of the 9th International Conference on Pattern Recognition Applications and Methods}}, keywords = {{Customer Reviews, Sentiment Analysis, Online Labour Markets}}, location = {{Valetta, Malta}}, pages = {{263----272}}, publisher = {{SCITEPRESS}}, title = {{{What Reviews in Local Online Labour Markets Reveal about the Performance of Multi-Service Providers}}}, year = {{2020}}, } @phdthesis{16935, author = {{Moussalem, Diego Campos}}, title = {{{Knowledge Graphs for Multilingual Language Translation and Generation}}}, doi = {{10.17619/UNIPB/1-980}}, year = {{2020}}, } @article{13770, author = {{Karl, Holger and Kundisch, Dennis and Meyer auf der Heide, Friedhelm and Wehrheim, Heike}}, journal = {{Business & Information Systems Engineering}}, number = {{6}}, pages = {{467--481}}, publisher = {{Springer}}, title = {{{A Case for a New IT Ecosystem: On-The-Fly Computing}}}, doi = {{10.1007/s12599-019-00627-x}}, volume = {{62}}, year = {{2020}}, } @inproceedings{3776, author = {{Chen, Wei-Fan and Al-Khatib, Khalid and Wachsmuth, Henning and Stein, Benno}}, booktitle = {{Proceedings of the Fourth Workshop on Natural Language Processing and Computational Social Science}}, pages = {{149--154}}, title = {{{Analyzing Political Bias and Unfairness in News Articles at Different Levels of Granularity}}}, year = {{2020}}, } @inproceedings{20137, author = {{Syed, Shahbaz and Chen, Wei-Fan and Hagen, Matthias and Stein, Benno and Wachsmuth, Henning and Potthast, Martin}}, booktitle = {{Proceedings of the 13th International Conference on Natural Language Generation (INLG 2020)}}, pages = {{237--241}}, title = {{{Task Proposal: Abstractive Snippet Generation for Web Pages}}}, year = {{2020}}, } @inproceedings{3818, author = {{Chen, Wei-Fan and Al-Khatib, Khalid and Stein, Benno and Wachsmuth, Henning}}, booktitle = {{Findings of the Association for Computational Linguistics: EMNLP 2020}}, pages = {{4290--4300}}, title = {{{Detecting Media Bias in News Articles using Gaussian Bias Distributions}}}, year = {{2020}}, } @inproceedings{15826, author = {{Chen, Wei-Fan and Syed, Shahbaz and Stein, Benno and Hagen, Matthias and Potthast, Martin}}, booktitle = {{Proceedings of the Web Conference 2020}}, pages = {{1309--1319}}, title = {{{Abstractive Snippet Generation}}}, year = {{2020}}, } @inproceedings{16868, author = {{Alshomary, Milad and Syed, Shahbaz and Potthast, Martin and Wachsmuth, Henning}}, booktitle = {{Proceedings of 58th Annual Meeting of the Association for Computational Linguistics (ACL 2020)}}, location = {{Seattle, USA}}, pages = {{4334--4345}}, publisher = {{Association for Computational Linguistics}}, title = {{{Target Inference in Argument Conclusion Generation}}}, year = {{2020}}, } @inproceedings{3287, abstract = {{For optimal placement and orchestration of network services, it is crucial that their structure and semantics are specified clearly and comprehensively and are available to an orchestrator. Existing specification approaches are either ambiguous or miss important aspects regarding the behavior of virtual network functions (VNFs) forming a service. We propose to formally and unambiguously specify the behavior of these functions and services using Queuing Petri Nets (QPNs). QPNs are an established method that allows to express queuing, synchronization, stochastically distributed processing delays, and changing traffic volume and characteristics at each VNF. With QPNs, multiple VNFs can be connected to complete network services in any structure, even specifying bidirectional network services containing loops. We discuss how management and orchestration systems can benefit from our clear and comprehensive specification approach, leading to better placement of VNFs and improved Quality of Service. Another benefit of formally specifying network services with QPNs are diverse analysis options, which allow valuable insights such as the distribution of end-to-end delay. We propose a tool-based workflow that supports the specification of network services and the automatic generation of corresponding simulation code to enable an in-depth analysis of their behavior and performance.}}, author = {{Schneider, Stefan Balthasar and Sharma, Arnab and Karl, Holger and Wehrheim, Heike}}, booktitle = {{2019 IFIP/IEEE International Symposium on Integrated Network Management (IM)}}, location = {{Washington, DC, USA}}, pages = {{116----124}}, publisher = {{IFIP}}, title = {{{Specifying and Analyzing Virtual Network Services Using Queuing Petri Nets}}}, year = {{2019}}, } @article{3585, abstract = {{Existing approaches and tools for the generation of approximate circuits often lack generality and are restricted to certain circuit types, approximation techniques, and quality assurance methods. Moreover, only few tools are publicly available. This hinders the development and evaluation of new techniques for approximating circuits and their comparison to previous approaches. In this paper, we first analyze and classify related approaches and then present CIRCA, our flexible framework for search-based approximate circuit generation. CIRCA is developed with a focus on modularity and extensibility. We present the architecture of CIRCA with its clear separation into stages and functional blocks, report on the current prototype, and show initial experiments.}}, author = {{Witschen, Linus Matthias and Wiersema, Tobias and Ghasemzadeh Mohammadi, Hassan and Awais, Muhammad and Platzner, Marco}}, issn = {{0026-2714}}, journal = {{Microelectronics Reliability}}, keywords = {{Approximate Computing, Framework, Pareto Front, Accuracy}}, pages = {{277--290}}, publisher = {{Elsevier}}, title = {{{CIRCA: Towards a Modular and Extensible Framework for Approximate Circuit Generation}}}, doi = {{10.1016/j.microrel.2019.04.003}}, volume = {{99}}, year = {{2019}}, } @inproceedings{7752, author = {{Sharma, Arnab and Wehrheim, Heike}}, booktitle = {{Proceedings of the Software Engineering Conference (SE)}}, isbn = {{978-3-88579-686-2}}, location = {{Stuttgart}}, pages = {{157 -- 158}}, publisher = {{Gesellschaft für Informatik e.V. (GI)}}, title = {{{Testing Balancedness of ML Algorithms}}}, volume = {{P-292}}, year = {{2019}}, } @misc{8312, author = {{Bäumer, Frederik Simon and Geierhos, Michaela}}, booktitle = {{encyclopedia.pub}}, keywords = {{OTF Computing, Natural Language Processing, Requirements Engineering}}, publisher = {{MDPI}}, title = {{{Requirements Engineering in OTF-Computing}}}, year = {{2019}}, } @misc{7623, author = {{Zhang, Shikun}}, pages = {{64}}, publisher = {{Universität Paderborn}}, title = {{{Combining Android Apps for Analysis Purposes}}}, year = {{2019}}, }