192 research outputs found
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
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
U-rank: Utility-oriented Learning to Rank with Implicit Feedback
Learning to rank with implicit feedback is one of the most important tasks in
many real-world information systems where the objective is some specific
utility, e.g., clicks and revenue. However, we point out that existing methods
based on probabilistic ranking principle do not necessarily achieve the highest
utility. To this end, we propose a novel ranking framework called U-rank that
directly optimizes the expected utility of the ranking list. With a
position-aware deep click-through rate prediction model, we address the
attention bias considering both query-level and item-level features. Due to the
item-specific attention bias modeling, the optimization for expected utility
corresponds to a maximum weight matching on the item-position bipartite graph.
We base the optimization of this objective in an efficient Lambdaloss
framework, which is supported by both theoretical and empirical analysis. We
conduct extensive experiments for both web search and recommender systems over
three benchmark datasets and two proprietary datasets, where the performance
gain of U-rank over state-of-the-arts is demonstrated. Moreover, our proposed
U-rank has been deployed on a large-scale commercial recommender and a large
improvement over the production baseline has been observed in an online A/B
testing
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