In this paper, we describe the combination of machine learning and simulation towards a hybrid modelling approach. Such a combination of data-based and knowledge-based modelling is motivated by applications that are partly based on causal relationships, while other effects result from hidden dependencies that are represented in huge amounts of data. Our aim is to bridge the knowledge gap between the two individual communities from machine learning and simulation to promote the development of hybrid systems. We present a conceptual framework that helps to identify potential combined approaches and employ it to give a structured overview of different types of combinations using exemplary approaches of simulation-assisted machine learning and machine-learning assisted simulation. We also discuss an advanced pairing in the context of Industry 4.0 where we see particular further potential for hybrid systems.

<div> <div> <div> <p> </p><div> <div> <div> <p>In this paper, we propose a new model named DIBERT which stands for Dependency Injected Bidirectional Encoder Representations from Transformers. DIBERT is a variation of the BERT and has an additional third objective called Parent Prediction (PP) apart from Masked Language Modeling (MLM) and Next Sentence Prediction (NSP). PP injects the syntactic structure of a dependency tree while pre-training the DIBERT which generates syntax-aware generic representations. We use the WikiText-103 benchmark dataset to pre-train both BERT- Base and DIBERT. After fine-tuning, we observe that DIBERT performs better than BERT-Base on various downstream tasks including Semantic Similarity, Natural Language Inference and Sentiment Analysis. </p> </div> </div> </div> </div> </div> </div>

Large organizations generate documents and records on a daily basis, often to such an extent that processing them manually becomes unduly time consuming. Because of this, automated processing systems for documents are desirable, as they would reduce the time spent handling them. Unfortunately, documents are often not designed to be machine-readable, so parsing them is a difficult problem. Image segmentation techniques and deep-learning architectures have been proposed as a solution to this, but have difficulty retaining accuracy when page layouts are especially dense. This leads to the possibilities of data being duplicated, lost, or inaccurate during retrieval. We propose a way of refining these segmentations, using a clustering based approach that can be easily combined with existing rules based refinements. We show that on a financial document corpus of 2675 pages, when using DBSCAN, this method is capable of significantly increasing the accuracy of existing deep-learning methods for image segmentation. This improves the reliability of the results in the context of automatic document analysis.

Financial reports are commonplace in the business world, but are long and tedious to produce. These reports mostly consist of tables with written sections describing these tables. Automating the process of creating these reports, even partially has the potential to save a company time and resources that could be spent on more creative tasks. We implement a transformer network to solve the task of generating this text. By generating matching pairs between tables and sentences found in financial documents, we created a dataset for our transformer. We were able to achieve promising results, with the final model reaching a BLEU score of 63.3. Generated sentences are natural, grammatically correct and mostly faithful to the information found in the tables.