Autonomous manipulation has enabled a wide range of exciting robot tasks. However, perceiving outside environment is still a challenging problem in the field of intelligent robotic research due to the lack of object models, unstructured environments, and time-consuming computation. In this article, we present a novel robot grasp detection system that maps a pair of RGB-D images of novel objects to best grasping pose of a robotic gripper. First, we segment the graspable objects from the unstructured scene using the geometrical features of both the object and the robotic gripper. Then, a deep convolutional neural network is applied on these graspable objects, which aims to find the best graspable area for each object. In order to improve the efficiency in the detection system, we introduce a structured penalty term to optimize the connections between multimodality, which significantly mitigates complexity of the network and outperforms fully connected multimodal processing. We also present a two-stage closed-loop grasping candidate estimator to accelerate the searching efficiency of grasping-candidate generation. Moreover, the combination of a two-stage estimator with the grasping detection network naturally improves detection accuracy. Experiments have been conducted to validate the proposed methods. The results show that our method outperforms the state of the art and runs at real-time speed.

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Ethereum is one of the currently popular trading platform, where any one can exchange, buy, or sell cryptocurrencies. Smart contract, a computer program, can help Ethereum to encode rules or scripts for processing transactions.Because the smart contract usually handles large number of cryptocurrencies worth billions of dollars apiece, its security has gained considerable attention.In this paper, we first investigate the security of smart contracts running on the Ethereum and introduce several new security vulnerabilities that allow adversaries to exploit and gain financial benefits. Then, we propose a more practical smart contract analysis tool termed NeuCheck, in which we introduce the syntax tree in the syntactical analyzer to complete the transformation from source code to intermediate representation, and then adopt the open source library working with XML to analyze such tree. We have built a prototype of NeuCheck for Ethereum and evaluate it with over 52 000 existing Ethereum smart contracts. The results show that (1) our new documented vulnerabilities are prevalent;(2) NeuCheck improves the analysis speed by at least 17.2 times compared to other popular analysis tools (eg, Securify and Mythril; and (3) allows for cross-platform deployment.

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