Community programs and women's participation : the Chinese experience

Using household data specifically collected for the purpose of evaluation, the authors empirically evaluate the impact on household income of a rural program in China that focuses on increasing women's economic and social participation in the local community. They find that the program substantially increases women's participation and household income, and also generates positive social benefits. The authors'results also suggest that the income gains accrue only to participants, and partly at the expense of nonparticipants. They find that the magnitude of the program's impact depends sensitively on the program's ability to increase participation rates within villages. In the presence of the program, individual participation helps to prevent negative externalities and to buy into the positive gains accruing to participants. The authors'results support the view that effectively implemented gender-focused interventions can have substantial social benefits when supported by the necessary legal and institutional framework.Decentralization,Primary Education,Public Health Promotion,Health Economics&Finance,Poverty Monitoring&Analysis,Health Economics&Finance,Poverty Monitoring&Analysis,Primary Education,Housing&Human Habitats,Governance Indicators

An advanced combination of semi-supervised Normalizing Flow & Yolo (YoloNF) to detect and recognize vehicle license plates

Fully Automatic License Plate Recognition (ALPR) has been a frequent research topic due to several practical applications. However, many of the current solutions are still not robust enough in real situations, commonly depending on many constraints. This paper presents a robust and efficient ALPR system based on the state-of-the-art YOLO object detector and Normalizing flows. The model uses two new strategies. Firstly, a two-stage network using YOLO and a normalization flow-based model for normalization to detect Licenses Plates (LP) and recognize the LP with numbers and Arabic characters. Secondly, Multi-scale image transformations are implemented to provide a solution to the problem of the YOLO cropped LP detection including significant background noise. Furthermore, extensive experiments are led on a new dataset with realistic scenarios, we introduce a larger public annotated dataset collected from Moroccan plates. We demonstrate that our proposed model can learn on a small number of samples free of single or multiple characters. The dataset will also be made publicly available to encourage further studies and research on plate detection and recognition.Comment: arXiv admin note: text overlap with arXiv:1802.09567 by other authors; text overlap with arXiv:2012.06737 by other authors without attributio

A Method for the Construction and Application of the Term Hierarchy Relationship Residing in Relevance Feedback

In the field of information retrieval, the information of term frequency contained in relevance feedback has been widely used. However, the analysis and application of term frequency does not cover the semantic meaning of the terms, which could make the retrieval results deviate from the user’s searching goal. Consider the semantic meaning of the terms, Wille (1992) had proposed a structured view in the dealing with the term relationships of the terms in the retrieval documents. To enhance the effectiveness of information retrieval by the dealing with the mentioned information of term hierarchy relationship, this study has developed a method of query expansion to extract and apply this information contained in relevance feedback first, and then conducted some formal tests to verify the efficiency of the method in the re-ranking of the retrieved documents. The results of the formal tests show that the proposed method of query expansion is more effective than the Rocchio’s query expansion algorithm. The contribution of this study is the disclosure of the applicability of the information of term hierarchy relationship contained in relevance feedback, and the demonstration of the application of this information

ReCOVery: A Multimodal Repository for COVID-19 News Credibility Research

First identified in Wuhan, China, in December 2019, the outbreak of COVID-19 has been declared as a global emergency in January, and a pandemic in March 2020 by the World Health Organization (WHO). Along with this pandemic, we are also experiencing an "infodemic" of information with low credibility such as fake news and conspiracies. In this work, we present ReCOVery, a repository designed and constructed to facilitate research on combating such information regarding COVID-19. We first broadly search and investigate ~2,000 news publishers, from which 60 are identified with extreme [high or low] levels of credibility. By inheriting the credibility of the media on which they were published, a total of 2,029 news articles on coronavirus, published from January to May 2020, are collected in the repository, along with 140,820 tweets that reveal how these news articles have spread on the Twitter social network. The repository provides multimodal information of news articles on coronavirus, including textual, visual, temporal, and network information. The way that news credibility is obtained allows a trade-off between dataset scalability and label accuracy. Extensive experiments are conducted to present data statistics and distributions, as well as to provide baseline performances for predicting news credibility so that future methods can be compared. Our repository is available at http://coronavirus-fakenews.com.Comment: Proceedings of the 29th ACM International Conference on Information and Knowledge Management (CIKM '20

Understanding or Manipulation: Rethinking Online Performance Gains of Modern Recommender Systems

Recommender systems are expected to be assistants that help human users find relevant information automatically without explicit queries. As recommender systems evolve, increasingly sophisticated learning techniques are applied and have achieved better performance in terms of user engagement metrics such as clicks and browsing time. The increase in the measured performance, however, can have two possible attributions: a better understanding of user preferences, and a more proactive ability to utilize human bounded rationality to seduce user over-consumption. A natural following question is whether current recommendation algorithms are manipulating user preferences. If so, can we measure the manipulation level? In this paper, we present a general framework for benchmarking the degree of manipulations of recommendation algorithms, in both slate recommendation and sequential recommendation scenarios. The framework consists of four stages, initial preference calculation, training data collection, algorithm training and interaction, and metrics calculation that involves two proposed metrics. We benchmark some representative recommendation algorithms in both synthetic and real-world datasets under the proposed framework. We have observed that a high online click-through rate does not necessarily mean a better understanding of user initial preference, but ends in prompting users to choose more documents they initially did not favor. Moreover, we find that the training data have notable impacts on the manipulation degrees, and algorithms with more powerful modeling abilities are more sensitive to such impacts. The experiments also verified the usefulness of the proposed metrics for measuring the degree of manipulations. We advocate that future recommendation algorithm studies should be treated as an optimization problem with constrained user preference manipulations.Comment: 33 pages, 11 figures, 4 tables, ACM Transactions on Information System

Large-scale Interactive Recommendation with Tree-structured Policy Gradient

Reinforcement learning (RL) has recently been introduced to interactive recommender systems (IRS) because of its nature of learning from dynamic interactions and planning for long-run performance. As IRS is always with thousands of items to recommend (i.e., thousands of actions), most existing RL-based methods, however, fail to handle such a large discrete action space problem and thus become inefficient. The existing work that tries to deal with the large discrete action space problem by utilizing the deep deterministic policy gradient framework suffers from the inconsistency between the continuous action representation (the output of the actor network) and the real discrete action. To avoid such inconsistency and achieve high efficiency and recommendation effectiveness, in this paper, we propose a Tree-structured Policy Gradient Recommendation (TPGR) framework, where a balanced hierarchical clustering tree is built over the items and picking an item is formulated as seeking a path from the root to a certain leaf of the tree. Extensive experiments on carefully-designed environments based on two real-world datasets demonstrate that our model provides superior recommendation performance and significant efficiency improvement over state-of-the-art methods

Digital twin modeling method based on IFC standards for building construction processes

Intelligent construction is a necessary way to improve the traditional construction method, and digital twin can be a crucial technology to promote intelligent construction. However, the construction field currently needs a unified method to build a standardized and universally applicable digital twin model, which is incredibly challenging in construction. Therefore, this paper proposes a general method to construct a digital twin construction process model based on the Industry Foundation Classes (IFC) standard, aiming to realize real-time monitoring, control, and visualization management of the construction site. The method constructs a digital twin fusion model from three levels: geometric model, resource model, and behavioral model by establishing an IFC semantic model of the construction process, storing the fusion model data and the construction site data into a database, and completing the dynamic interaction of the twin data in the database. At the same time, the digital twin platform is developed to realize the visualization and control of the construction site. Combined with practical cases and analysis, the implementation effect of the method is shown and verified. The results show that the method can adapt itself to different scenarios on the construction site, which is conducive to promoting application of the digital twin in the field of construction and provides a reference to the research of practicing digital twin theory and practice