Titlebook: Discovery Science; 26th International C Albert Bifet,Ana Carolina Lorena,Pedro H. Abreu Conference proceedings 2023 The Editor(s) (if appli

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Deane E. Neubauer,Shingo Ashizawas utilizing advanced pre-trained Natural Language Inference (NLI) models offer a viable solution when training data is unavailable. This work introduces the . framework, an unsupervised stance detection framework based on Zero-Shot Learning. It detects a five-valued user’s stance on social-political

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https://doi.org/10.1007/978-3-658-21246-9t is commonly made by malicious users or automated programs (i.e., bots) that mimic human behaviour. With the recent boom of online review systems, performing accurate review spam detection has become of primary importance for a review platform, to mitigate the effect of malicious users responsible

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https://doi.org/10.1007/978-3-658-28931-7ing tasks, current methods often rely on pre-trained neural language models. Using these models, the state-of-the-art supervised systems for key-phrase extraction require large amounts of labelled data and generalize poorly outside the training domain, while unsupervised approaches generally present

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Justus Junkermann,Ludwig Goldhahnlier detection is the task of individuating anomalous objects within a given data population they belong to. In this paper we propose a new technique to explain why a given data object has been singled out as anomalous. The explanation our technique returns also includes counterfactuals, each of whi

ARCH

Bastian Baumeister,Roger Bergeruch capabilities are crucially important with the rising adoption of AI models in real-world applications, which require domain experts to understand how model predictions are extracted in order to make informed decisions. Despite the increasing number of XAI approaches for tabular, image, and graph

火光在摇曳

https://doi.org/10.1007/978-3-658-28931-7atisfied, and finally makes a decision by comparing the total score to a threshold. Scoring systems have a long history of active use in safety-critical domains such as healthcare and justice, where they provide guidance for making objective and accurate decisions. Given their genuine interpretabili

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Justus Junkermann,Ludwig Goldhahn high-dimensional data sets in two-dimensional maps, using embeddings of data objects under exploration and representing their temporal relations with directed edges. Most existing dimensionality reduction techniques, such as t-SNE and UMAP, disregard the temporal or relational nature of the data du

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Eignet sich meine Arbeit zur Publikation?ocuses mainly on enabling computers to manipulate and generate human language, whereas TSA identifies patterns or components in time-dependent data. Given their different purposes, there has been limited exploration of combining them. In this study, we present an approach to convert text into time s