The real-time detection of pine cones in Korean pine forests is not only the data basis for the mechanized picking of pine cones, but also one of the important methods for evaluating the yield of Korean pine forests. In recent years, there has been a certain number of detection accuracy for image processing of fruits in trees using deep-learning methods, but the overall performance of these methods has not been satisfactory, and they have never been used in the detection of pine cones. In this paper, a pine cone detection method based on Boundary Equilibrium Generative Adversarial Networks (BEGAN) and You Only Look Once (YOLO) v3 mode is proposed to solve the problems of insufficient data set, inaccurate detection result and slow detection speed. First, we use traditional image augmentation technology and generative adversarial network BEGAN to implement data augmentation. Second, we introduced a densely connected network (DenseNet) structure in the backbone network of YOLOv3. Third, we expanded the detection scale of YOLOv3, and optimized the loss function of YOLOv3 using the Distance-IoU (DIoU) algorithm. Finally, we conducted a comparative experiment. The experimental results show that the performance of the model can be effectively improved by using BEGAN for data augmentation. Under same conditions, the improved YOLOv3 model is better than the Single Shot MultiBox Detector (SSD), the faster-regions with convolutional neural network (Faster R-CNN) and the original YOLOv3 model. The detection accuracy reaches 95.3%, and the detection efficiency is 37.8% higher than that of the original YOLOv3.

Wireless Sensor Networks (WSN) is often deployed in hostile environments, and the attacker can easily capture nodes to inject false data to the sensor network, which can cause serious results. This paper has studied various false data filtering techniques recently in wireless sensor network. Based on encryption technology, we have analyzed and compared the difference of various existing filtering strategies, then have pointed out the merits and demerits of them in detailed. At last, we give the developing trend of false data filtering, which provides a strong foundation for the further research.

Achieving the rapid and accurate detection of pine cones in the natural environment is essential for yield estimation and automatic picking. However, the complex background and tiny target pose a significant challenge to pine cone detection. This paper proposes a pine cone detection method using the improved You Only Look Once (YOLO) version 4 algorithm to overcome these challenges. First, the original pine cone image data come from a natural pine forest. Crawler technology is utilized to collect more pine cone images from the Internet to expand the data set. Second, the densely connected convolution network (DenseNet) structure is introduced in YOLOv4 to improve feature reuse and network performance. In addition, the backbone network is pruned to reduce the computational complexity and keep the output dimension unchanged. Finally, for the problem of feature fusion at different scales, an improved neck network is designed using the scale‐equalizing pyramid convolution (SEPC). The experimental results show that the improved YOLOv4 model is better than the original YOLOv4 network; the average values of precision, recall, and AP reach 96.1%, 90.1%, and 95.8%; the calculation amount of the model is reduced by 21.2%; the detection speed is fast enough to meet the real‐time requirements. This research could serve as a technical reference for estimating yields and automating the picking of pine cones.

This paper introduces the cyber infrastructure and e-Science application practices for wild birds' monitoring and protection in Qinghai Lake region. We have been building a cyber infrastructure to support the protection and research in this area since 2006. We introduce the e-Science application practices supporting research on species identification, spatial distribution patterns analysis, and avian influenza virus cross-species infection by presenting their methods, implementation, and results. This is the first attempt in China to construct cyber infrastructure to support protection and research on wild birds in plateau area. The e-Science application practices are also the first one applied in the national nature reserve in China.

There are various techniques have been proposed for constructing neural ranking models, including representation-based models and interaction-based models, both employ shallow or deep neural networks to tackle the reranking problem in document retrieval, in which query is usually the first met object before we focus on documents. Thus query can be treated as target when modeling the matching patterns between it and documents, however, few works have attempted to do that. In this paper we propose a target-oriented transformation networks based neural ranking model TTRM, which encodes the target information into the document content and utilizing a context conserving transformation to encapsulate the contextualized features. By this way, can the model produce a new target-derived word level representations for the document, from which the salient abstract features can be extracted using a convolution network, and the final relevance score between the query-document pair can be obtained by stacking a feed forward network. The comparison results show that our proposed model can outperform the baseline models on two important evaluation metrics when conducting experiments on the benchmark collection.

Virtual Observatory (VO) is a collection of interoperating data archives and software tools. Taking advantages of the latest information technologies, it aims to provide a data-intensively online research environment for astronomers all around the world. A large number of high-qualified astronomical software packages and libraries are powerful and easy of use, and have been widely used by astronomers for many years. Integrating those toolkits into the VO system is a necessary and important task for the VO developers. VO architecture greatly depends on Grid and Web services, consequently the general VO integration route is "Java Ready – Grid Ready – VO Ready". In the paper, we discuss the importance of VO integration for existing toolkits and discuss the possible solutions. We introduce two efforts in the field from China-VO project, "gImageMagick" and "Galactic abundance gradients statistical research under grid environment". We also discuss what additional work should be done to convert Grid service to VO service.