The social protection of rural workers in the construction industry in urban China

The construction industry is important for Chinese rural to urban migrants. Over 90% of urban construction workers are rural migrants, and over a third of all rural migrants work in construction. The construction industry is not only particularly important, but is also different from other industries in its pay and labour recruitment practices. In common with other rural workers, construction workers have long suffered from various problems, including delayed payment of salaries and exclusion from urban social security schemes. State policies designed to deal with these problems have in general had mixed success. Partly as a result of the peculiarities of the construction industry, state policy has been particularly unsuccessful in dealing with the problems faced by construction workers. This paper considers both the risks rural workers in the construction industry face because of the work they do and the risks they face and because of their being rural workers. It shows that social protection needs to take into account both the work related risks and status related risks. The authors first review the literature concerning work related risks, and then build up a framework to analyse the risks embedded in their work and status, and the relationship between these risks and the existing formal social protection. Thirty one in depth interviews with construction workers, carried out in Tianjin, PRC, are used to demonstrate both the risks and the inability of the state-led social policy to tackle these risks. The results suggest that rural construction workers in cities were exposed to all sorts of problems from not being paid for their work in time to miserable living conditions, from having to pay for their own healthcare to no savings for old age. This paper highlights the problems of policy prescriptions that failed to recognise the complexity of the problems faced by these workers and criticises the tendency to seek quick fixes rather than long-term and careful institutional design

Distributed data fusion algorithms for inertial network systems

New approaches to the development of data fusion algorithms for inertial network systems are described. The aim of this development is to increase the accuracy of estimates of inertial state vectors in all the network nodes, including the navigation states, and also to improve the fault tolerance of inertial network systems. An analysis of distributed inertial sensing models is presented and new distributed data fusion algorithms are developed for inertial network systems. The distributed data fusion algorithm comprises two steps: inertial measurement fusion and state fusion. The inertial measurement fusion allows each node to assimilate all the inertial measurements from an inertial network system, which can improve the performance of inertial sensor failure detection and isolation algorithms by providing more information. The state fusion further increases the accuracy and enhances the integrity of the local inertial states and navigation state estimates. The simulation results show that the two-step fusion procedure overcomes the disadvantages of traditional inertial sensor alignment procedures. The slave inertial nodes can be accurately aligned to the master node

The Social Protection of Rural Workers in the Construction Industry in Urban China

The construction industry is important for Chinese rural to urban migrants. Over 90% of urban construction workers are rural migrants, and over a third of all rural migrants work in construction. The construction industry is not only particularly important, but is also different from other industries in its pay and labour recruitment practices. In common with other rural workers, construction workers have long suffered from various problems, including delayed payment of salaries and exclusion from urban social security schemes. State policies designed to deal with these problems have in general had mixed success. Partly as a result of the peculiarities of the construction industry, state policy has been particularly unsuccessful in dealing with the problems faced by construction workers. This paper considers both the risks rural workers in the construction industry face because of the work they do and the risks they face and because of their being rural workers. It shows that social protection needs to take into account both the work related risks and status related risks. The authors first review the literature concerning work related risks, and then build up a framework to analyse the risks embedded in their work and status, and the relationship between these risks and the existing formal social protection. Thirty one in depth interviews with construction workers, carried out in Tianjin, PRC, are used to demonstrate both the risks and the inability of the state-led social policy to tackle these risks. The results suggest that rural construction workers in cities were exposed to all sorts of problems from not being paid for their work in time to miserable living conditions, from having to pay for their own healthcare to no savings for old age. This paper highlights the problems of policy prescriptions that failed to recognise the complexity of the problems faced by these workers and criticises the tendency to seek quick fixes rather than long-term and careful institutional design.social security, rural-urban migrants, construction workers, industrial organisation, social exclusion, People’s Republic of China, work related risks

Learning Dynamic Classes of Events using Stacked Multilayer Perceptron Networks

People often use a web search engine to find information about events of interest, for example, sport competitions, political elections, festivals and entertainment news. In this paper, we study a problem of detecting event-related queries, which is the first step before selecting a suitable time-aware retrieval model. In general, event-related information needs can be observed in query streams through various temporal patterns of user search behavior, e.g., spiky peaks for popular events, and periodicities for repetitive events. However, it is also common that users search for non-popular events, which may not exhibit temporal variations in query streams, e.g., past events recently occurred, historical events triggered by anniversaries or similar events, and future events anticipated to happen. To address the challenge of detecting dynamic classes of events, we propose a novel deep learning model to classify a given query into a predetermined set of multiple event types. Our proposed model, a Stacked Multilayer Perceptron (S-MLP) network, consists of multilayer perceptron used as a basic learning unit. We assemble stacked units to further learn complex relationships between neutrons in successive layers. To evaluate our proposed model, we conduct experiments using real-world queries and a set of manually created ground truth. Preliminary results have shown that our proposed deep learning model outperforms the state-of-the-art classification models significantly.Comment: Neu-IR '16 SIGIR Workshop on Neural Information Retrieval, 6 pages, 4 figure

A Corpus-based Comparison between the Academic Word List and the Academic Vocabulary List

This study was a corpus-based comparison between two lists of academic words: Coxhead’s (2000) Academic Word List (AWL) and Gardner and Davies’ (2014) Academic Vocabulary List (AVL). Comparisons were made between different types of lexical coverage provided by the AWL and the AVL in the self-created University Academic Corpus (72-million tokens). The findings indicated that the performance of the AWL and the AVL was different when different evaluation criteria were adopted. Implications, limitations, and suggestions are listed for future research

A Study on Building a “Three Wholes” Long-Term Mechanism for Tuition-Free Normal University Students’ Education on Career Aspiration

The establishment and achievement of tuition-free normal university students’ lofty and unswerving teaching aspirations is closely associated with the implementation of the state tuition-free policy for normal university students. It has a direct impact on the improvement of the quality of China’s future education, and is the core of cultivating tuition-free normal students. In this sense, the education in terms of their career aspirations plays a vital role. The paper is of the belief that building a long-term “three wholes” mechanism which includes whole process, whole participation and whole direction is an inherent requirement of enhancing the effectiveness of the career aspiration education on the part of tuition-free normal university students

A comprehensive study of sparse codes on abnormality detection

Sparse representation has been applied successfully in abnormal event detection, in which the baseline is to learn a dictionary accompanied by sparse codes. While much emphasis is put on discriminative dictionary construction, there are no comparative studies of sparse codes regarding abnormality detection. We comprehensively study two types of sparse codes solutions - greedy algorithms and convex L1-norm solutions - and their impact on abnormality detection performance. We also propose our framework of combining sparse codes with different detection methods. Our comparative experiments are carried out from various angles to better understand the applicability of sparse codes, including computation time, reconstruction error, sparsity, detection accuracy, and their performance combining various detection methods. Experiments show that combining OMP codes with maximum coordinate detection could achieve state-of-the-art performance on the UCSD dataset [14].Comment: 7 page

ACO-GCN: A FAULT DETECTION FUSION ALGORITHM FOR WIRELESS SENSOR NETWORK NODES

Wireless Sensor Network (WSN) has become a solution for real-time monitoring environments and is widely used in various fields. A substantial number of sensors in WSNs are prone to succumb to failures due to faulty attributes, complex working environments, and their hardware, resulting in transmission error data. To resolve the existing problem of fault detection in WSN, this paper presents a WSN node fault detection method based on ant colony optimization-graph convolutional network (ACO-GCN) models, which consists of an input layer, a space-time processing layer, and an output layer. First, the users apply the random search algorithm and the search strategy of the ant colony algorithm (ACO) to find the optimal path and locate the WSN node failures to grasp the overall situation. Then, the WSN fault node information obtained by the GCN model is learned. During the data training process, where the WSN fault node is used for error prediction, the weights and thresholds of the network are further adjusted to increase the accuracy of fault diagnosis. To evaluate the performance of the ACO-GCN model, the results show that the ACO-GCN model significantly improves the fault detection rate and reduces the false alarm rate compared with the benchmark algorithms. Moreover, the proposed ACO-GCN fusion algorithm can identify fault sensors more effectively, improve the service quality of WSN and enhance the stability of the system