10,624 research outputs found

    Innovative Asia: Advancing the Knowledge-Based Economy - Highlights of the Forthcoming ADB Study Report

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    [Excerpt] The development of knowledge-based economies (KBEs) is both an imperative and an opportunity for developing Asia. It is an imperative to sustain high rates of growth in the future and an opportunity whereby emerging economies can draw from beneficial trending developments that may allow them to move faster to advance in global value chains and in position in world markets. Over the last quarter of a century, driven mostly by cheap labor, developing countries in Asia have seen unprecedented growth rates and contributions to the global economy. Sustaining Asia’s growth trajectory, however, requires developing economies to seek different approaches to economic growth and progress, especially if they aspire to move from the middle-income to the high-income level. KBE is an important platform that can enable them to sustain growth and even accelerate it. It is time for Asia to consolidate and accelerate its pace of growth. Asia is positioned in a unique moment in history with many advantages that can serve as a boost: to name a couple, an expanding middle of the pyramid—Asia is likely to hold 50% of the global middle class and 40% of the global consumer market by 2020; and the growing importance of intra-regional trade within Asia, increasing from 54% in 2001 to 58% in 2011. Many developing economies are well placed to assimilate frontier technologies into their manufacturing environment

    Afterschool for the Global Age

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    Summarizes discussions from a July 2006 convening on model afterschool programs and best practices for enhancing global literacy, including innovative uses of community and international connections, project-based learning, and educational technology

    False News On Social Media: A Data-Driven Survey

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    In the past few years, the research community has dedicated growing interest to the issue of false news circulating on social networks. The widespread attention on detecting and characterizing false news has been motivated by considerable backlashes of this threat against the real world. As a matter of fact, social media platforms exhibit peculiar characteristics, with respect to traditional news outlets, which have been particularly favorable to the proliferation of deceptive information. They also present unique challenges for all kind of potential interventions on the subject. As this issue becomes of global concern, it is also gaining more attention in academia. The aim of this survey is to offer a comprehensive study on the recent advances in terms of detection, characterization and mitigation of false news that propagate on social media, as well as the challenges and the open questions that await future research on the field. We use a data-driven approach, focusing on a classification of the features that are used in each study to characterize false information and on the datasets used for instructing classification methods. At the end of the survey, we highlight emerging approaches that look most promising for addressing false news

    Automated Discovery of Internet Censorship by Web Crawling

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    Censorship of the Internet is widespread around the world. As access to the web becomes increasingly ubiquitous, filtering of this resource becomes more pervasive. Transparency about specific content that citizens are denied access to is atypical. To counter this, numerous techniques for maintaining URL filter lists have been proposed by various individuals and organisations that aim to empirical data on censorship for benefit of the public and wider censorship research community. We present a new approach for discovering filtered domains in different countries. This method is fully automated and requires no human interaction. The system uses web crawling techniques to traverse between filtered sites and implements a robust method for determining if a domain is filtered. We demonstrate the effectiveness of the approach by running experiments to search for filtered content in four different censorship regimes. Our results show that we perform better than the current state of the art and have built domain filter lists an order of magnitude larger than the most widely available public lists as of Jan 2018. Further, we build a dataset mapping the interlinking nature of blocked content between domains and exhibit the tightly networked nature of censored web resources
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