This poster introduces HarvETHter, a smart contract decompiler for EVM-based platforms such as Ethereum, Binance, and Polygon. We present the corpus completeness hypothesis, which we investigate through HarvETHter. Relying on our hypothesis, HarvETHter sources knowledge of the Ethereum blockchain and leverages it to decompile smart contracts to Solidity source code.

Artificial intelligence (AI) is used more heavily in agricultural applications.Yet, the lack of wireless-fidelity (Wi-Fi) connections on agricultural fields makes AI cloud services unavailable.Consequently, AI models have to be processed directly on the edge.In this paper, we evaluate state-of-the-art detection algorithms for their use in agriculture, in particular plant detection.Thus, this paper presents the CornWeed data set, which has been recorded on farm machines, showing labelled maize crops and weeds for plant detection.The paper provides accuracies for the state-of-the-art detection algorithms on the CornWeed data set, as well as frames per second (FPS) metrics for the considered networks on multiple edge devices.Moreover, for the FPS analysis, the detection algorithms are converted to open neural network exchange (ONNX) and TensoRT engine files as they could be used as future standards for model exchange.