Auflistung nach Schlagwort "classification"
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- KonferenzbeitragAre “Non-functional” Requirements really Non-functional?(Software Engineering 2017, 2017) Eckhardt, Jonas; Vogelsang, Andreas; Fernández, Daniel MéndezNon-functional requirements (NFRs) are commonly distinguished from functional requirements (FRs) by differentiating how the system shall do something in contrast to what the system shall do. This distinction is not only prevalent in research, but also influences how requirements are handled in practice. NFRs are usually documented separately from FRs, without quantitative mea- sures, and with relatively vague descriptions. As a result, they remain difficult to analyze and test. Several authors argue, however, that many so-called NFRs actually describe behavioral properties and may be treated the same way as FRs. In this paper, we empirically investigate this point of view and aim to increase our understanding on the nature of NFRs addressing system properties. Our re- sults suggest that most “non-functional” requirements are not non-functional as they describe behavior of a system. Consequently, we argue that many so-called NFRs can be handled similarly to FRs.
- KonferenzbeitragAssessment of ground conditions in grassland on a mower with artificial intelligence(44. GIL - Jahrestagung, Biodiversität fördern durch digitale Landwirtschaft, 2024) Manss, Christoph; Martel, Viktor; Weisgerber, RomanProcess-monitoring for autonomous mowers in agriculture is crucial to establish an online quality assessment. Here, neural networks (NNs) are employed to classify ground conditions, distinguishing between dry, mowed, unplanted, and grass. The data comprises RGB images that are captured by a camera mounted on a mower. These images are then used to train various NNs, with EfficientNet_V2_s emerging as the most accurate network and with ResNet18 to be the most efficient network in terms of training duration and accuracy. The study also reveals for this use-case that employing transfer learning enhances the overall network performance. The developed NNs is intended for deployment on mowers, enabling them to adjust their mowing blades, conserve energy, and enhance the quality of mowed grass. Beyond mowing, the NN can be applied in process control and the identification of other plant species or weeds in the agricultural field, contributing to biodiversity assessments and more sustainable farming practices.
- KonferenzbeitragBlue Apple – an algorithm to realize agricultural classification under difficult light and color situations(43. GIL-Jahrestagung, Resiliente Agri-Food-Systeme, 2023) Credner, Jonas; Rehrmann, Peter; Raaz, Waldemar; Rath, ThomasComputer-image processing becomes more and more important in the analysis of data in biological and agricultural research and practice. However, robust image processing is highly dependent on the histogram analysis algorithms used and the quality of the data being processed. The algorithm presented here aims to improve the accuracy of the classification of image data generated under complex boundary situations and inconsistent lighting conditions. Using the example of the determination of nitrogen content of tomato leaves and the qualitative determination of starch content of apples on the basis of color image processing, we showed that the developed algorithm is able to perform a robust classification and represents an improvement to simple histogram analysis.
- WorkshopbeitragEffective Toxicity Prediction in Online Multiplayer Gaming: Four Obstacles to Making Approaches Usable(Mensch und Computer 2022 - Workshopband, 2022) Frommel, Julian; Mandryk, ReganToxicity represents a threat to the safety and health of online multiplayer gaming communities. This has been recognized by industry, academia, and players and led to efforts for combating toxicity, including different approaches for predicting toxicity from behaviour. Despite promising results, such approaches have not yet been able to meaningfully combat toxicity at scale. In this position paper, we describe four obstacles that impede usable applied toxicity prediction in multiplayer games that could help to combat harm.We want to foster a discussion about how user-centered artificial intelligence approaches may help solve these obstacles.
- ZeitschriftenartikelEfficient machine learning for attack detection(it - Information Technology: Vol. 62, No. 5-6, 2020) Wressnegger, ChristianDetecting and fending off attacks on computer systems is an enduring problem in computer security. In light of a plethora of different threats and the growing automation used by attackers, we are in urgent need of more advanced methods for attack detection. Manually crafting detection rules is by no means feasible at scale, and automatically generated signatures often lack context, such that they fall short in detecting slight variations of known threats. In the thesis “Efficient Machine Learning for Attack Detection” [35], we address the necessity of advanced attack detection. For the effective application of machine learning in this domain, a periodic retraining over time is crucial. We show that with the right data representation, efficient algorithms for mining substring statistics, and implementations based on probabilistic data structures, training the underlying model for establishing an higher degree of automation for defenses can be achieved in linear time.
- TextdokumentExploring the Use of the Pronoun I in German Academic Texts with Machine Learning(INFORMATIK 2020, 2021) Andresen, Melanie; Knorr, DagmarThe use of the pronoun ich (‘I’) in academic language is a source of constant debate and a frequent cause of insecurity for students. We explore manually annotated instances of I from a German learner corpus. Using machine learning techniques, we investigate to what extent it is possible to automatically distinguish between different types of I usage (author I vs. narrator I). We additionally inspect which context words are good indicators of one type or the other. The results show that an automatic classification is not straightforward, but the distinctive features are in line with previous research. The results of the automatic classification are not perfect, but would greatly facilitate manual annotation. The distinctive words are in line with previous research and indicate that the author I is a more homogeneous class.
- KonferenzbeitragFrom Automated to On-The-Fly Machine Learning(INFORMATIK 2019: 50 Jahre Gesellschaft für Informatik – Informatik für Gesellschaft, 2019) Mohr, Felix; Wever, Marcel; Tornede, Alexander; Hüllermeier, Eyke
- ZeitschriftenartikelSentiStorm: Echtzeit-Stimmungserkennung von Tweets(HMD Praxis der Wirtschaftsinformatik: Vol. 53, No. 4, 2016) Zangerle, Eva; Illecker, Martin; Specht, GüntherDas automatisierte Erkennen der Stimmung von Texten hat in den letzten Jahren stark an Bedeutung gewonnen. Insbesondere durch die rapide Zunahme der Geschwindigkeit, mit der in sozialen Medien Informationen verbreitet werden, ist eine Echtzeit-Bestimmung der Stimmung von Texten ein herausforderndes Problem. Der Mikroblogging-Dienst Twitter verzeichnet im Durchschnitt über 8000 versendete Nachrichten pro Sekunde. In dieser Arbeit stellen wir mit dem SentiStorm-Ansatz einen Ansatz zur Stimmungserkennung von Tweets vor. Dabei erzeugen wir in einem ersten Schritt Merkmalsvektoren für die Tweets, die sowohl linguistische Informationen über den Tweet (Wichtigkeit der Wörter, Wortarten), wie auch über Sentiment-Lexika gewonnene Stimmungsinformationen beinhalten. In einem zweiten Schritt führen wir mittels der Merkmalsvektoren eine Stimmungsklassifikation durch, die eine Einteilung in positive, negative oder neutrale Tweets ermöglicht. Die durchgeführten Evaluationen zeigen, dass der präsentierte Ansatz bezüglich der Qualität der erkannten Stimmung sehr gute Erkennungsraten garantiert. Weiter zeigen wir, dass der Ansatz mittels der Apache Storm Plattform problemlos für die Echtzeit-Stimmungserkennung von Tweets skaliert werden kann.AbstractThe automatic detection of the sentiment of texts has become more and more important throughout the last years. Particularly, the rapid increase of the speed at which information is spread in social media makes real-time sentiment detection a challenging task. On the microblogging platform Twitter, more than 8,000 messages are sent every second. In this work, we present the SentiStorm approach, an approach for sentiment detection within tweets. We base the approach on feature vectors which contain linguistic information about the tweet content (weighting of words, word categories), as well as sentiment information which we gather based on sentiment lexica. Subsequently, we facilitate these feature vectors for a sentiment classification task which allows for distinguishing positive, negative and neutral tweets. Our conducted evaluations show that the proposed approach shows high classification accuracy. At the same time, we show that utilizing the Apache Storm platform we are able to easily scale the approach towards a real-time sentiment classification of tweets.
- WorkshopbeitragTagStar: ein interaktives Indexierungs- und Analysewerkzeug(Mensch & Computer 2012: interaktiv informiert – allgegenwärtig und allumfassend!?, 2012) de Almeida Madeira Clemente, Mirko; Keck, Mandy; Groh, RainerDieser Beitrag beschreibt das interaktive Visualisierungskonzept TagStar, welches der computergestützten und kollaborativen Erschließung von Visualisierungen und der Analyse des daraus resultierenden Datenbestandes dient. Die Visualisierungen können durch Hinzufügen von Schlagwörtern eines zugrundeliegenden Klassifikationsschemas klassifiziert und semantisch beschrieben werden. Unterstützung bieten Schlagwortempfehlungen und eine Icon-basierte Darstellungstechnik.
- KonferenzbeitragA Tool for Human-in-the-Loop Analysis and Exploration of (not only) Prosodic Classifications for Post-modern Poetry(INFORMATIK 2019: 50 Jahre Gesellschaft für Informatik – Informatik für Gesellschaft (Workshop-Beiträge), 2019) Baumann, Timo; Hussein, Hussein; Meyer-Sickendiek, Burkhard; Elbeshausen, JasperData-based analyses are becoming more and more common in the Digital Humanities and tools are needed that focus human efforts on the most interesting and important aspects of exploration, analysis and annotation by using active machine learning techniques. We present our ongoing work on a tool that supports classification tasks for spoken documents (in our case: read-out post-modern poetry) using a neural networks-based classification backend and a web-based exploration and classification environment.