Since economic reform in the 1980s, Chinese sport has undergone an extraordinary transformation. The most distinguishing phenomenon is the rapid growth of mass sport at the grassroots level with increasing demands for physical activities in women's daily lives. The rapid growth of women's sports participation at the grassroots is deeply embedded in the process of social stratification as a result of the urbanisation of Chinese society. The purpose of this paper is to use the socialist, feminist and theoretical framework to explore how Chinese women's different economic, educational, domestic and cultural situations shape their sports values and patterns of participation, marking social boundaries in Chinese urban communities. Semi-structured interviews and observations were conducted with 60 female physical exercisers in sports clubs, parks and neighbourhood playgrounds. Documentary research was also applied as a complement method to the interview. The findings indicate that within different classes (middle class, working class and a group who were unemployed), many different opportunities for and limitations on women to participate in sport are noticed. Chinese women have not fully and equally utilised sports opportunities created by urbanisation. Most Chinese women still live within patriarchal arrangements. Consequently, they do not completely fulfil their ambitions in sport.
This paper constructs an automated identification platform for phishing URLs in mobile message. This platform have the ability that including preprocessing the content of message, feature extraction and automatic identification of URLs in the message, output the result. Aiming at the problem of automatic identification of phishing web site, this paper propose a phishing URLs recognition model based on neural networks and deep learning. The experimental results show that that the model has a very good judgment effect on the phishing URLs, and the accuracy rate is 98.2%. The recall rate is 96.9%.
Abstract Due to the scarcity and uncertainty of anomalies, anomaly detection becomes a challenging problem in communities. This paper proposes an effective time series anomaly detection network based on prediction and adversarial training. First, we employ LSTM model based on differential attention mechanism to fully extract the inner characters of time series. To prevent the over-fitting due to only normal samples used to train our LSTM model, we introduce an adversarial training strategy by adding adversarial samples to enhance the generalization of the model on normal data. The effectiveness of our model is demonstrated on several public datasets.
Abstract With the development of mobile devices, more and more location-based services (LBS) on the devices are needed and fingerprint indoor localization has become one most important technique because of its low cost and high accuracy. In this paper, we use the fingerprinting method which based on Channel State Information (CSI) for indoor localization. Furthermore, we extract the raw phase information from the multiple antennas and multiple sub-carriers through the IEEE 802.11n network interface card (NIC 5300) on several special models. Then we extract the required phase information and introduce two methods of mathematical statistics to analyse the feature of CSI signals. We replace the processed phase information with the signal features obtained from the analysis. For the offline stage, we employ a deep network with three hidden layers to train the signal features data, and use weights to represent fingerprints. Introducing a greedy learning algorithm to train the weights layer-by-layer to reduce the computational complexity, and the sub-network between two continuous layers forms a restricted Boltzmann machine (RBM). For the online location estimation, we use a probabilistic method based on the radial basis function (RBF). The neural network method we used this time is tested under two different scenarios, and different data are compared. The final conclusion is that the new method we combined is superior to the previous.
This paper presents, discusses and evaluates empirical studies concerned with gender differences in religion. Within the psychology of religion two main groups of theories have been advanced to account for gender differences in religiosity. The first group of theories concentrates on social or contextual influences which shape different responses to religion among men and women. This group may be divided into two categories: gender role socialisation theories and structural location theories. The second group of theories concentrates on personal or individual psychological characteristics which differentiate between men and women. This group may be divided into three categories: depth psychology theories, personality theories and gender orientation theories. It is concluded that gender orientation theories provide the most fruitful source for further research.
In ET58 (Apr 99), there appeared an article entitled ‘English everywhere in China’, in which Kang Jianxiu has not only cited many examples to show that English is extensively used in China, but also lists several reasons for using the language and predicts that the phenomenon is unstoppable. From the date of its publication to the present time, more than six years have passed. What has happened in China during those years concerning the use of English? Has Kang's predication been proved right? We have been following the phenomenon, and would like to discuss these questions.