Text classification is one of the most important and typical tasks in Natural Language Processing (NLP) which can be applied for many applications. Recently, deep learning approaches has shown their advantages in solving text classification problem, in which Convolutional Neural Network (CNN) is one of the most successful model in the field. In this paper, we propose a novel deep learning approach for categorizing text documents by using scope-based convolutional neural network. Different from windowbased CNN, scope does not require the words that construct a local feature have to be contiguous. It can represent deeper local information of text data. We propose a large-scale scope-based convolutional neural network (LSS-CNN), which is based on scope convolution, aggregation optimization, and max pooling operation. Based on these techniques, we can gradually extract the most valuable local information of the text document. This paper also discusses how to effectively calculate the scope-based information and parallel training for large-scale datasets. Extensive experiments have been conducted on real datasets to compare our model with several state-of-the-art approaches. The experimental results show that LSS-CNN can achieve both effectiveness and good scalability on big text data.

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The problem of trip planning has received wide concerns in recent years. More and more people require the service of automatically confirming the optimal tour route. When users assign the source and the destination, and the time limit of the tour, how can automatically decide the optimal tour route with the highest sum of the popularity scores of scenic spots. Current methods for trip planning are on the setting that providing with the route which is composed of the scenic spots to travel. These would work poorly for the pre-mentioned problem when the route satisfying the constraints can not be found. Thus we adjust the setting to giving the route composed of the scenic spots which users visit or simply pass by. Obviously, the modified problem would incur larger search cost as each scenic spot in the given route has two states. It can be demonstrated that this new problem is NP hard, making it difficult to find an efficient exact algorithm for the present. In this paper, we propose a greedy strategy based algorithm to solve the trip planning problem, and we also present an improved algorithm with better performance. The experimental results on synthesized and real data sets reveal that our algorithm is able to find the approximately optimal path in high efficiency.

During the Covid-19 global pandemic, exposure to cold cargo surfaces contaminated with Covid-19 was first identified as a potential cause of infection. Given that the epidemic situation in China is basically stable, epidemic prevention and control of cold chain cargo handling operations in Chinese ports is one of the key points for the country. It is concluded that the main risk link that may cause the spread of the epidemic is the unpacking of cold chain cargo containers by analyzing the process and characteristics of port cold chain cargo handling.In order to prevent imported epidemics from abroad, Chinese ports have taken countermeasures such as virus detection and sterilization. At present, nucleic acid detection measures have been adopted for the virus detection on the surface of goods, but the sampling quantity and method are lack of unified regulations, and the detection takes a long time. The mobile cabin PCR laboratories are used in some areas to improve the timeliness, and the virus detection on the surface of goods needs more sensitive and rapid detection technology. In the process of comprehensive preventive disinfection of goods, it was found that the disinfection efficiency of common disinfectants in low-temperature environment was greatly reduced, and a variety of new lowtemperature disinfectants were rapidly developed. The disinfection technology based on deep UV LED, UV catalysis, nuclear radiation and other physical technologies have brought a new revolution to the disinfection of the new coronavirus on the surface of low-temperature objects.Due to the global pandemic of novel coronavirus and its continuous variation, technical measures for epidemic prevention and control have developed rapidly. From the prevention and control experience of Chinese ports in combating the epidemic, epidemic prevention and control is a systematic project, which requires the combination of various technical measures and close cooperation of multiple links.