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Inverse synthetic aperture radar (ISAR) is a popular space object detection method. However, due to the complex flight conditions of spacecraft and limited observation time, the resolution of spacecraft ISAR images is reduced. Although there are many methods of ISAR image super resolution, but none of them aim at spacecraft. To this end, in this letter, we analyze the characteristics of spacecraft ISAR images, and first propose an approach space super resolution network (SSRN), suitable for spacecraft ISAR images. Specially, our approach is designed to utilize the repetitive structures commonly observed in spacecraft ISAR images. So we introduce nonlocal sparse attention (NLSA) to capture the long-distance self-similarity in the spacecraft ISAR image. Spherical locality sensitive hashing (SLSH) is used to construct multiple attention buckets, and the query feature and features in the same and adjacent buckets are used for attention operations. To effectively extract multiscale features and contrast features from spacecraft ISAR image, we design the Residual Atrous Spatial Pyramid Pooling block (ResASPPblock), connect a number of atrous convolution layers with different dilation rates, and add the skip connection. The experiment on the real ISAR image of a certain type spacecraft proves the effectiveness of our approach, and the performance of our approach is higher than the popular ISAR super resolution method. Overall, our approach provides a promising solution for improving the resolution of spacecraft ISAR images. [ABSTRACT FROM AUTHOR]

Web services are products in the era of service-oriented computing and cloud computing. Considering the information overload problem arising from the task of selecting web services, a recommendation system is by far the most effective solution for performing such selections. However, users calling a limited number of services will cause severe data sparseness and a weak correlation with services. In addition, fully mining the semantic features and knowledge features of the text description is also a major problem that needs to be solved urgently. This paper proposes a deep knowledge-aware approach which introduces knowledge graph and knowledge representation into web service recommendation for the first time. We solve the data sparse problem and optimize the user's feature representation. In this approach, an attention module is introduced to model the impact of tags for the candidate services on different words of user queries, and a deep neural network is used to model the high-level features of user-service invocation behaviors. The results of experiments demonstrate that the proposed approach can achieve better recommendation performance than other state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Social networks have evolved into a popular information and communication platform, and the vast amount of data it generates are rapidly changing and spreading. Thus, it is essential to detect and trace large events and burst topics in mass social network data based on real-time Big Data parallel computing. In this paper, we propose a model that uses the Negative Binomial Distribution to fit the distribution of Weibo topic words. Then, we introduce the concepts of the 'hot degree' and the 'dispersion degree' of a topic with their corresponding computing methods. And we validate the efficiency of the model using real data. Furthermore, we design a topic detection and trend-tracing algorithm based on stream data, and implement the algorithm on Spark Streaming which is a streaming processing framework that uses memory computing. Finally, the experiments on real data demonstrate that our proposal is effective and efficient in tracking bursting events. [ABSTRACT FROM AUTHOR]

Meteorological hazards have great influence all over the world. An emergency plan is an important means of coping with meteorological hazards. The preparation of emergency plans needs to refer to historical emergency plans, but these are too numerous and are of uneven quality. We can alleviate these problems by means of recommender systems, which are very useful tools in many domains; however, they suffer from information overload. In this paper, we propose a Semantic-Aware Collaborative Filtering method, which is called SACF, for emergency plans recommendation to address the aforementioned challenges. It is designed to effectively present a highly targeted emergency plan recommendation list and recommend the most appropriate emergency plans for a targeted meteorological hazards event. Specifically, we use semantic knowledge to represent scenario-based meteorological hazards, including target and previous events. The search for similar events (i.e., neighbors) for a collaborative filtering recommendation algorithm is adopted. By helping to avoid both the generation of fake neighbors and also the omission of true neighbors the recommendation process is improved. Finally, extensive experiments are conducted on a real-world dataset, and the results demonstrate that SACF improves the accuracy of emergency plan recommendations. [ABSTRACT FROM AUTHOR]

Natural language query systems over RDF data need to rely on the semantic relations in query. First, we propose the new crowdsourcing model that used to produce semantic relations dataset. The model not only inherits completeness of the iterative model and accuracy of the parallel model, but also saves human resources. Second, we mine the rules of semantic relation recognition from the correlations between dependency structures and semantic relations. Third, we propose an algorithm of semantic relation recognition for natural language query over RDF data, and experiments demonstrate that it can recognize more semantic relations than existing methods. [ABSTRACT FROM AUTHOR]

The quick and accurate identification of post-earthquake rescue objects can minimize the casualties and property loss caused by earthquakes. With the rapid development of remote sensing technology, rescue objects can be identified through high-resolution remote sensing images. There are two main categories of approaches to identify rescue object through high-resolution images: automatic extraction by a computer and visual judgment by professionals. Although results can be obtained quickly by using automatic extraction, the accuracy of the results is unacceptably low. For visual judgment, the large demands for time and professionals restrict its wide practical application. In this study, we introduce crowdsourcing into the identification of post-earthquake rescue objects. First, we integrate the advantages of the computer and crowdsourcing, which means that the computer takes advantage of the speed of information processing, while crowdsourcing makes full use of human recognition capabilities. Second, we take visual judgment tasks out of the hands of professionals and entrust the tasks to workers in a crowdsourcing platform. Not only are the human resources infinite, but we can also improve the efficiency of identifying rescue objects. Third, we propose a crowdsourcing model that improves the quality of the results and saves human resources. Finally, experimental results demonstrate that our solution is feasible. [ABSTRACT FROM AUTHOR]

Crowdsourcing is a new emerging distributed computing and business model on the backdrop of Internet blossoming. With the development of crowdsourcing systems, the data size of crowdsourcers, contractors and tasks grows rapidly. The worker quality evaluation based on big data analysis technology has become a critical challenge. This paper first proposes a general worker quality evaluation algorithm that is applied to any critical tasks such as tagging, matching, filtering, categorization and many other emerging applications, without wasting resources. Second, we realize the evaluation algorithm in the Hadoop platform using the MapReduce parallel programming model. Finally, to effectively verify the accuracy and the effectiveness of the algorithm in a wide variety of big data scenarios, we conduct a series of experiments. The experimental results demonstrate that the proposed algorithm is accurate and effective. It has high computing performance and horizontal scalability. And it is suitable for large-scale worker quality evaluations in a big data environment. [ABSTRACT FROM AUTHOR]