Traditional Batik production relies on skilled artisans to plan multistage dyeing sequences that yield culturally meaningful and visually harmonious color combinations. This manual practice is time-consuming, difficult to document, and hard to scale to novice designers, and prior work on Batik color design has rarely treated the dyeing process explicitly as a learnable temporal sequence. This study addresses that gap by modeling Batik dyeing as a sequence learning problem and applying a Recurrent Neural Network with Long Short-Term Memory (RNN–LSTM) to support automated color design. We construct a dataset of 72 fabric samples obtained from single- and two-color dyeing procedures that follow traditional Batik wax–dye–dewax workflows. For each sample, RGB values are extracted at each dyeing stage and encoded as time-ordered inputs, while the final fabric colors are used as target outputs. The proposed RNN–LSTM learns to predict harmonious color sequences that are consistent with examples in the dataset. It achieves a prediction accuracy of 0.869 on held-out data, outperforming several feedforward and recurrent neural network baselines under the same training protocol. An interactive simulation interface then integrates the trained model, allowing users to explore and visualize predicted color outcomes step-by-step. The results show how AI-based sequence modeling can help preserve Batik color traditions while making expert color design strategies more accessible.
With the Data Governance Act (hereinafter DGA), the EU is introducing data intermediation service providers (DISP) to increase trust and promote data sharing through so-called neutral middlemen. This article focuses on the potential consequences of falling under the scope of the DGA and consequently the stringent obligations contained in this regulation. Specifically, the unbundling and neutrality obligations could have a major impact on established and new data intermediation business models in that regard. It is problematic that the DGA leaves room for gray areas regarding its scope and consequently its obligations for DISPs. Indeed, different interpretations may have different consequences in practice regarding the viability of a business models of a company. This may even cause friction in the market by potentially making conditions more favorable for one company than another in certain cases.
The European Commission’s digital single market policies are becoming increasingly concerned with the impact of so-called ‘platforms’ on competition in the internal market. Whereas the Commission acknowledges the contributions of platform companies to innovation and consumer welfare, it also sees actual and potential damages occurring from their powerful position. As such, the Commission aims to strengthen the enforcement of its competition law rules in this area. We do not focus on the actual outcome of the application of competition law, but more so on the claims made about the pro- and anti-competitive effects of platforms that inform both agenda-setting and actual decision-making. After analysing four case studies we came to the conclusion that the Commission, in these cases is (1) recognizing the platform circumstance as their focus is more on B2B relations rather than B2C; (2) focusing more on behavioural than structural effects; (3) finding it difficult to hand out the right mix of remedies in ex-ante regulation; (4) somewhat understanding of the impact of network effects; (5) quite complex in their analysis especially for software-based mergers. Finally, we observe that the Commission’s stance is largely inspired by legal and economic experts and public interest concerns are largely missing from the debate.
