No abstract

Traditional pose similarity functions based on joint coordinates or rotations often do not conform to human perception. We propose a new perceptual pose distance: Relational Geometric Distance that accumulates the differences over a set of features that reflects the geometric relations between different body parts. An extensive relational geometric feature pool that contains a large number of potential features is defined, and the features effective for pose similarity estimation are selected using a set of labeled data by Adaboost. The extensive feature pool guarantees that a wide diversity of features is considered, and the boosting ensures that the selected features are optimized when used jointly. Finally, the selected features form a pose distance function that can be used for novel poses. Experiments show that our method outperforms others in emulating human perception in pose similarity. Our method can also adapt to specific motion types and capture the features that are important for pose similarity of a certain motion type.

To solve the problems that existing network security situation assessment (NSSA) methods are difficult to extract features and have poor timeliness, an NSSA method with network attack behavior classification (NABC) is proposed. First, an NABC model is designed. The model combines features and advantages of a parallel feature extraction network (PFEN), a bidirectional gate recurrent unit (BiGRU), and the attention mechanism (ATT). The PFEN module is composed of parallel sparse autoencoders which extract key data from different network attack behaviors. The BiGRU module gets the time-series relationship from the state of three different time periods, finds potential representation rules from network attack behaviors. The ATT module pays more attention to the network traffic key information and improves the NABC accuracy. Second, the NABC detects and classifies attacks from network behaviors, the occurrence number of each attack behavior, and the error probability matrix are counted. Finally, the occurrence number of each attack behavior is corrected according to the error probability matrix, and the network security situation value is calculated through combining the severity factor of each attack behavior. The experimental results show that the precision and recall of the NABC model are improved by 5.28% and 5.65%, respectively, compared with the conventional method. The comparison experiment with the

BOPPPS, based on constructivism and communicative approach, is famous for effective teaching. It is a closed-loop teaching activity model that emphasizes students’ participation and feedback in the process of classroom teaching organization. Taking the course of Intensive English as an example, the paper designs teaching plan based on BOPPPS, formulates teaching tasks and implementation approaches in line with BOPPPS, implements the teaching plan, and reflects the teaching effect through teaching practice. Taking features of Intensive Reading course into consideration, the paper claims that BOPPPS is an effective teaching model, which not only stimulates students’ self-directed learning, but also save teachers and students’ energy to solve puzzles within the limited credit periods. Hopefully, BOPPPS might be gradually and widely applied to the English teaching.

The mechanical tests of normal concrete (NC) specimens, steel fiber reinforced concrete (SFRC) specimens and polypropylene fiber reinforced concrete (PPFRC) specimens have been carried out. Fiber-reinforced concretes containing different volume fraction and aspect ratio of steel and polypropylene fibers were compared in terms of compressive, splitting tensile, ultimate tensile properties. Test results indicate that the mechanical properties of NC can be improved by addition of steel fibers and can be enhanced with the increase of fiber content. However, polypropylene fiber may cause opposite effect, if volume fraction too high.