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

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

Understanding Hidden Memories of Recurrent Neural Networks

Recurrent neural networks (RNNs) have been successfully applied to various natural language processing (NLP) tasks and achieved better results than conventional methods. However, the lack of understanding of the mechanisms behind their effectiveness limits further improvements on their architectures. In this paper, we present a visual analytics method for understanding and comparing RNN models for NLP tasks. We propose a technique to explain the function of individual hidden state units based on their expected response to input texts. We then co-cluster hidden state units and words based on the expected response and visualize co-clustering results as memory chips and word clouds to provide more structured knowledge on RNNs' hidden states. We also propose a glyph-based sequence visualization based on aggregate information to analyze the behavior of an RNN's hidden state at the sentence-level. The usability and effectiveness of our method are demonstrated through case studies and reviews from domain experts.Comment: Published at IEEE Conference on Visual Analytics Science and Technology (IEEE VAST 2017

Allocating Limited Resources to Protect a Massive Number of Targets using a Game Theoretic Model

Resource allocation is the process of optimizing the rare resources. In the area of security, how to allocate limited resources to protect a massive number of targets is especially challenging. This paper addresses this resource allocation issue by constructing a game theoretic model. A defender and an attacker are players and the interaction is formulated as a trade-off between protecting targets and consuming resources. The action cost which is a necessary role of consuming resource, is considered in the proposed model. Additionally, a bounded rational behavior model (Quantal Response, QR), which simulates a human attacker of the adversarial nature, is introduced to improve the proposed model. To validate the proposed model, we compare the different utility functions and resource allocation strategies. The comparison results suggest that the proposed resource allocation strategy performs better than others in the perspective of utility and resource effectiveness.Comment: 14 pages, 12 figures, 41 reference

AdaVis: Adaptive and Explainable Visualization Recommendation for Tabular Data

Automated visualization recommendation facilitates the rapid creation of effective visualizations, which is especially beneficial for users with limited time and limited knowledge of data visualization. There is an increasing trend in leveraging machine learning (ML) techniques to achieve an end-to-end visualization recommendation. However, existing ML-based approaches implicitly assume that there is only one appropriate visualization for a specific dataset, which is often not true for real applications. Also, they often work like a black box, and are difficult for users to understand the reasons for recommending specific visualizations. To fill the research gap, we propose AdaVis, an adaptive and explainable approach to recommend one or multiple appropriate visualizations for a tabular dataset. It leverages a box embedding-based knowledge graph to well model the possible one-to-many mapping relations among different entities (i.e., data features, dataset columns, datasets, and visualization choices). The embeddings of the entities and relations can be learned from dataset-visualization pairs. Also, AdaVis incorporates the attention mechanism into the inference framework. Attention can indicate the relative importance of data features for a dataset and provide fine-grained explainability. Our extensive evaluations through quantitative metric evaluations, case studies, and user interviews demonstrate the effectiveness of AdaVis

Predicting student performance in interactive online question pools using mouse interaction features

Modeling student learning and further predicting the performance is a well-established task in online learning and is crucial to personalized education by recommending different learning resources to different students based on their needs. Interactive online question pools (e.g., educational game platforms), an important component of online education, have become increasingly popular in recent years. However, most existing work on student performance prediction targets at online learning platforms with a well-structured curriculum, predefined question order and accurate knowledge tags provided by domain experts. It remains unclear how to conduct student performance prediction in interactive online question pools without such well-organized question orders or knowledge tags by experts. In this paper, we propose a novel approach to boost student performance prediction in interactive online question pools by further considering student interaction features and the similarity between questions. Specifically, we introduce new features (e.g., think time, first attempt, and first drag-and-drop) based on student mouse movement trajectories to delineate students' problem-solving details. In addition, heterogeneous information network is applied to integrating students' historical problem-solving information on similar questions, enhancing student performance predictions on a new question. We evaluate the proposed approach on the dataset from a real-world interactive question pool using four typical machine learning models.Comment: 10 pages, 7 figures, conference lak20, has been accepted, proceeding now. link: https://lak20.solaresearch.org/list-of-accepted-paper

Child population, economic development and regional inequality of education resources in China

There is great inequality of educational resources between different provinces in China due to unbalanced economic development. Despite continued redistribution of financial resources by the central government in favor of poorer provinces, educational inequality remains. In this paper, we argue that focusing on educational resources is far from sufficient. Poorer provinces do not only suffer from a lower level of educational resources, but they also have more children to educate, i.e. a greater need for education. Combining and analyzing the data in the Sixth National Population Census of China and the official statistics on education spending and resources, we found that provincial-level variations in the child population and the child dependency ratio have made access to educational resources even more unequal given the unequal financial capacity at the provincial level. Poorer provinces face a higher child dependency ratio and have lower economic development, and these two factors jointly lead to limited educational resources. Apart from a much higher level of redistribution in favor of less developed provinces, encouraging more balanced distribution of teachers and more broadly promoting economic equality are essential to reduce inequality in educational resources in China

Visualizing research impact through citation data

Research impact plays a critical role in evaluating the research quality and influence of a scholar, a journal, or a conference. Many researchers have attempted to quantify research impact by introducing different types of metrics based on citation data, such as h -index, citation count, and impact factor. These metrics are widely used in the academic community. However, quantitative metrics are highly aggregated in most cases and sometimes biased, which probably results in the loss of impact details that are important for comprehensively understanding research impact. For example, which research area does a researcher have great research impact on? How does the research impact change over time? How do the collaborators take effect on the research impact of an individual? Simple quantitative metrics can hardly help answer such kind of questions, since more detailed exploration of the citation data is needed. Previous work on visualizing citation data usually only shows limited aspects of research impact and may suffer from other problems including visual clutter and scalability issues. To fill this gap, we propose an interactive visualization tool, ImpactVis , for better exploration of research impact through citation data. Case studies and in-depth expert interviews are conducted to demonstrate the effectiveness of ImpactVis . </jats:p

Peer-inspired student performance prediction in interactive online question pools with graph neural network

Student performance prediction is critical to online education. It can benefit many downstream tasks on online learning platforms, such as estimating dropout rates, facilitating strategic intervention, and enabling adaptive online learning. Interactive online question pools provide students with interesting interactive questions to practice their knowledge in online education. However, little research has been done on student performance prediction in interactive online question pools. Existing work on student performance prediction targets at online learning platforms with predefined course curriculum and accurate knowledge labels like MOOC platforms, but they are not able to fully model knowledge evolution of students in interactive online question pools. In this paper, we propose a novel approach using Graph Neural Networks (GNNs) to achieve better student performance prediction in interactive online question pools. Specifically, we model the relationship between students and questions using student interactions to construct the student-interaction-question network and further present a new GNN model, called R^2GCN, which intrinsically works for the heterogeneous networks, to achieve generalizable student performance prediction in interactive online question pools. We evaluate the effectiveness of our approach on a real-world dataset consisting of 104,113 mouse trajectories generated in the problem-solving process of over 4000 students on 1631 questions. The experiment results show that our approach can achieve a much higher accuracy of student performance prediction than both traditional machine learning approaches and GNN models.Comment: 8 pages, 8 figures. Accepted at CIKM 202