Paths Forward for the Global Water, Sanitation, and Hygiene (WASH) Sector

The report summarizes key challenges and recommendations discussed at an event entitled "Paths Forward for the Global Water, Sanitation, and Hygiene (WASH) Sector" hosted by the Global Water Futures Project at the Center for Strategic and International Studies (CSIS). The series of discussions focused on ways to catalyze and strengthen efforts to address international WASH problems. The sessions aimed to develop a set of actionable recommendations to improve the outcomes of global WASH programs and to increase the capacity of the U.S.-based public and private sectors to engage in program activities related to global WASH challenges. Each session examined a key challenge facing the water, sanitation, and hygiene sector. Roundtables focused on the following themes: "Building the Momentum for WASH Awareness," "Growing the Resource Base for WASH Efforts," "Making Our WASH Investments Count," and "Breaking the WASH Silo.

Leveraging Deep Learning Techniques on Collaborative Filtering Recommender Systems

With the exponentially increasing volume of online data, searching and finding required information have become an extensive and time-consuming task. Recommender Systems as a subclass of information retrieval and decision support systems by providing personalized suggestions helping users access what they need more efficiently. Among the different techniques for building a recommender system, Collaborative Filtering (CF) is the most popular and widespread approach. However, cold start and data sparsity are the fundamental challenges ahead of implementing an effective CF-based recommender. Recent successful developments in enhancing and implementing deep learning architectures motivated many studies to propose deep learning-based solutions for solving the recommenders' weak points. In this research, unlike the past similar works about using deep learning architectures in recommender systems that covered different techniques generally, we specifically provide a comprehensive review of deep learning-based collaborative filtering recommender systems. This in-depth filtering gives a clear overview of the level of popularity, gaps, and ignored areas on leveraging deep learning techniques to build CF-based systems as the most influential recommenders.Comment: 24 pages, 14 figure

Bridging communities of practice: Emerging technologies for content-centered linking

The project fosters convergence between two communities by addressing complementary aspects of a shared opportunity. Digital humanists are at the forefront of developing ways to render cultural heritage metadata increasingly interoperable as linked open data in tandem with information professionals working in libraries, archives, and museums. Computer scientists are developing automated techniques for extracting linkable data from the content itself. Bringing these communities together offers transformational potential for the application of a critical infrastructure in humanities scholarship. Two workshops will be organized to seize this unique opportunity. The first will bring together humanities scholars and computer scientists to explore applications of new content linking technologies to dispersed and disparate material. In the second, a larger group of humanities scholars will identify specific content to which techniques described in the previous workshop will be applied

The Rise of Innovation Districts: A New Geography of Innovation in America

As the United States slowly emerges from the great recession, a remarkable shify is occurring in the spatial geogrpahy of innovation. For the past 50 years, the landscape of innovation has been dominated by places like Silicon Valley - suburban corridors of spatially isolated corporate campuses, accessible only by car, with little emphasis on the quality of life or on integrating work, housing, and recreation. A new complementary urban model is now emerging, giving rise to what we and others are calling "innovation districts." These districts, by our definition, are geographic areas where leading-edge anchor institutions and companies cluster and connect with start-ups, business incubators, and accelerators. They are also physically compact, transit-accessible, and technicall

Building Intellectual Capital in Incubated Technology Firms

Purpose: The value of relational capital generated by entrepreneurs with their internal and external environment (Hormiga, Batista-Canino and Sanchez-Medina, 2011), provides considerable resources when properly leveraged. It is particularly important in environments such as the high tech sector of incomplete information and weak economic markets such as new products, markets or technologies (Davidsson, and Honig, 2003). The paper examines how incubated technology entrepreneurs build relational capital for a new venture formation in the social context of a Higher Education Institution. Design/methodology/approach: Our study took a qualitative approach based on content analysis of business plans and in-depth interviews with twenty-five technology entrepreneurs on an incubation programme – South East Enterprise Platform Programme - for technology graduates in the South East of Ireland. Findings: Our study found that technology entrepreneurs during new venture formation engaged in four types of relational capital activities, namely, development of networks and contacts, relationship building, accessing and leveraging knowledge experts and members of associations. Practical Implications: Incubator programmes need to actively support social building activities of technology entrepreneurs. HEI knowledge assets and networks are critical elements in supporting incubator technology entrepreneurs. Originality/Value: Our study identified four types of relational capital building. We also found using Evans Jones (1995) categorization of technology entrepreneurs that users, producers, opportunists and non–technical entrepreneurs engaged in client focused relational capital building, whereas researcher types networked with service providers and displayed arms length relational capital building styles

Trip Prediction by Leveraging Trip Histories from Neighboring Users

We propose a novel approach for trip prediction by analyzing user's trip histories. We augment users' (self-) trip histories by adding 'similar' trips from other users, which could be informative and useful for predicting future trips for a given user. This also helps to cope with noisy or sparse trip histories, where the self-history by itself does not provide a reliable prediction of future trips. We show empirical evidence that by enriching the users' trip histories with additional trips, one can improve the prediction error by 15%-40%, evaluated on multiple subsets of the Nancy2012 dataset. This real-world dataset is collected from public transportation ticket validations in the city of Nancy, France. Our prediction tool is a central component of a trip simulator system designed to analyze the functionality of public transportation in the city of Nancy

The Digitalisation of African Agriculture Report 2018-2019

An inclusive, digitally-enabled agricultural transformation could help achieve meaningful livelihood improvements for Africa’s smallholder farmers and pastoralists. It could drive greater engagement in agriculture from women and youth and create employment opportunities along the value chain. At CTA we staked a claim on this power of digitalisation to more systematically transform agriculture early on. Digitalisation, focusing on not individual ICTs but the application of these technologies to entire value chains, is a theme that cuts across all of our work. In youth entrepreneurship, we are fostering a new breed of young ICT ‘agripreneurs’. In climate-smart agriculture multiple projects provide information that can help towards building resilience for smallholder farmers. And in women empowerment we are supporting digital platforms to drive greater inclusion for women entrepreneurs in agricultural value chains