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Found 7 papers, 5 papers with code
Guiding Text-to-Text Privatization by Syntax
Lacking the capability to produce surrogate texts that correlate with the structure of the sensitive texts, we encompass our analysis by transforming the privatization step into a candidate selection problem in which substitutions are directed to words with matching grammatical properties.
Driving Context into Text-to-Text Privatization
\textit{Metric Differential Privacy} enables text-to-text privatization by adding calibrated noise to the vector of a word derived from an embedding space and projecting this noisy vector back to a discrete vocabulary using a nearest neighbor search.
GAM(e) changer or not? An evaluation of interpretable machine learning models based on additive model constraints
The number of information systems (IS) studies dealing with explainable artificial intelligence (XAI) is currently exploding as the field demands more transparency about the internal decision logic of machine learning (ML) models.
Time Matters: Time-Aware LSTMs for Predictive Business Process Monitoring
To better model the time dependencies between events, we propose a new PBPM technique based on time-aware LSTM (T-LSTM) cells.
Prescriptive Business Process Monitoring for Recommending Next Best Actions
We present a PrBPM technique that transforms the next most likely activities into the next best actions regarding a given KPI.
XNAP: Making LSTM-based Next Activity Predictions Explainable by Using LRP
PBPM techniques aim to improve process performance by providing predictions to process analysts, supporting them in their decision making.
A Technique for Determining Relevance Scores of Process Activities using Graph-based Neural Networks
Through annotations with metrics such as the frequency or duration of activities, these models provide generic information to the process analyst.
