Click carefully before you quote : citing internet-based sources

At the end of the 20th century, access to information provided by the World Wide Web (WWW) is changing as never before. The fast availability of current medical literature and the availability of tools for easy access to information, as well as for the easy production of information, have confronted research physicians, scholars, and students with new kinds of problems, many of which concern us personally. Quality control, difficulty establishing basic citation components, lack of standard guidelines for citing, as well as the short lifetime of Internet addresses concern us deeply. Some of these problems could be solved by the concept of an "Online-Library of Medicine" presented in the following paper. Since, however, at the present time there are no good answers to the problems regarding citing Internet-based sources, a Web surfer must keep in his or her mind the motto “caveat lector” (let the reader beware) - or, rather, in the spirit of our time: click c@refully before you cite

Forage News [2016-10]

KY Grazing Conference Write UpTribute to a Forage Legend: Warren ThompsonFrost and Freezes Increase Cyanide Poisoning RiskKentucky Cattlemen Association Presents Forage Specialist Funds at the KFGC Field DayForage News Quote of the Month: Beware of Emotional Decision - Russell HackleyFall Nitrogen Boosts Winter Survival and Spring GreenupMaster Grazer\u27s new Coordinator, Jacob BrandenburgFeatured Publication: Calculating Carrying Capacity Using Web Soil Survey, AGR-22

An app for personal searches:more-private, non-volatile searches with stigmergic inspiration

Web searching has long become a ubiquitous behavior amongst Internet users. Much has been changing: odds are that the thousands of results of yesterday have become millions of results today, but did that significant jump in quantity translate to an increase in the perceived results' quality and their applications? Some users might feel personalization efforts as stereotypification or even as inaccurate biases; they may also beware that every click on every search result may reinforce and contribute to (in)accurate representation of them - and would prefer searching without tracking. "Personal Searcher" is a work-in-progress app that makes it possible to search more anonymously. It also makes it possible to keep a private local-only history of one's searches and build personal ranking systems based on that history and other data. The goal is to benefit from local offline personalization, but search online as anonymously as possible.N/

Fundraising: Keys to the Cashbox

This special fundraising report for performing arts organizations discusses a variety of nonprofit trends. Articles cover the use of social media, competitive fundraising using games, data usage, kickstarter, legal issues in fundraising, and individual giving

With the Support of Listeners Like You : Lessons from U.S. Public Radio

This chapter provides an assessment of public broadcasting in the United States. It asserts that European public service broadcasting (PSB) could learn from U.S. practices that may prove to be particularly relevant in the current PSB climate

Bandits Warm-up Cold Recommender Systems

We address the cold start problem in recommendation systems assuming no contextual information is available neither about users, nor items. We consider the case in which we only have access to a set of ratings of items by users. Most of the existing works consider a batch setting, and use cross-validation to tune parameters. The classical method consists in minimizing the root mean square error over a training subset of the ratings which provides a factorization of the matrix of ratings, interpreted as a latent representation of items and users. Our contribution in this paper is 5-fold. First, we explicit the issues raised by this kind of batch setting for users or items with very few ratings. Then, we propose an online setting closer to the actual use of recommender systems; this setting is inspired by the bandit framework. The proposed methodology can be used to turn any recommender system dataset (such as Netflix, MovieLens,...) into a sequential dataset. Then, we explicit a strong and insightful link between contextual bandit algorithms and matrix factorization; this leads us to a new algorithm that tackles the exploration/exploitation dilemma associated to the cold start problem in a strikingly new perspective. Finally, experimental evidence confirm that our algorithm is effective in dealing with the cold start problem on publicly available datasets. Overall, the goal of this paper is to bridge the gap between recommender systems based on matrix factorizations and those based on contextual bandits