Recent researches on mobile robots show that convolutional neural network (CNN) has achieved impressive performance in visual place recognition especially for large-scale dynamic environment. However, CNN leads to the large space of image representation that cannot meet the real-time demand for robot navigation. Aiming at this problem, we evaluate the feature effectiveness of feature maps obtained from the layer of CNN by variance and propose a novel method that reserve salient feature maps and make adaptive binarization for them. Experimental results demonstrate the effectiveness and efficiency of our method. Compared with state of the art methods for visual place recognition, our method not only has no significant loss in precision, but also greatly reduces the space of image representation. key words: visual place recognition, CNN, variance, feature map, binarization Yutian Chen is master candidate in Army Engineering University of PLA. His main research fields are deep learning and image processing.

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Because multiple domain cyberspace joint attacks are becoming more widespread, establishing a multiple domain cyberspace defensive paradigm is becoming more vital. However, although some physical domain and social domain information is incorporated in present approaches, total modeling of cyberspace is absent, therefore thorough modeling of cyberspace is becoming increasingly necessary. This paper proposed a knowledge graph based multiple domain cyberspace modeling approach. A knowledge graph of multiple domain cyberspace is produced by extracting multiple domain entity information and entity relations such as physical domain, social domain, network domain, and information domain, so that semantic information of multiple domain cyberspace may be described consistently. At the same time, this paper proposed a user’s permissions reasoning method based on multiple domain cyberspace knowledge graph to address the user’s permissions reasoning that relies on artificial reasoning principles. Through the model learning knowledge graph triples characteristics and rules, and implementing automatic reasoning of user’s permissions, this proposed method can abandon the artificial model of writing reasoning rules, allowing the machine to learn the reasoning rules using machine learning and other methods. Experimental results showed that the proposed method can learn relevant reasoning rules and accomplish automated reasoning of user’s permissions, and that the method’s accuracy and recall rates are higher than those of path ranking and translating embeddings.