Use of Start Pages for Building a Mashup Personal Learning Environment to Support Self-Organized Learners

The paper explores the functionalities of eight start pages and considers their usefulness when used as a mashable platform for deployment of personal learning environments (PLE) for self-organized learners. The Web 2.0 effects and eLearning 2.0 strategies are examined from the point of view of how they influence the methods of gathering and capturing data, information and knowledge, and the learning process. Mashup technology is studied in order to see what kind of components can be used in PLE realization. A model of a PLE for self-organized learners is developed and it is used to prototype a personal learning and research environment in the start pages Netvibes, Pageflakes and iGoogle.

Early stage start-ups : evidence from the Comprehensive Australian Study of Entrepreneurial Emergence (CAUSEE)

In this chapter, the picture of Australian small business is supplemented by using data from the Comprehensive Australian Study of Entrepreneurial Emergence (CAUSEE) . This data tracks large numbers of on-going business start-ups over time. The Australian Centre of Entrepreneurship Research at Queensland University of Technology collected data in four annual waves. (Wave 1 to Wave 4) from 2007 to 2011. CAUSEE allows the analysis of entrepreneurial entrants at two stages of development, i.e. nascent and young firms. Nascent firms are defined as firms in the process of being created, but not yet established in the market, and young firms are defined as having been operational for up to four years. An analysis of nascent firms provides unique insights, as no other known Australian database captures and follows the development of business start-ups at the pre-operational stage. In addition, the project captured judgment over samples of high-potential start-ups.

Web Data Extraction from Query Result Pages Based on Visual and Content Features

A rapidly increasing number of Web databases are now become accessible via
their HTML form-based query interfaces. Query result pages are dynamically generated
in response to user queries, which encode structured data and are displayed for human
use. Query result pages usually contain other types of information in addition to query
results, e.g., advertisements, navigation bar etc. The problem of extracting structured data
from query result pages is critical for web data integration applications, such as comparison
shopping, meta-search engines etc, and has been intensively studied. A number of approaches
have been proposed. As the structures of Web pages become more and more complex, the
existing approaches start to fail, and most of them do not remove irrelevant contents which
may a®ect the accuracy of data record extraction. We propose an automated approach for
Web data extraction. First, it makes use of visual features and query terms to identify data
sections and extracts data records in these sections. We also represent several content and
visual features of visual blocks in a data section, and use them to ¯lter out noisy blocks.
Second, it measures similarity between data items in di®erent data records based on their
visual and content features, and aligns them into di®erent groups so that the data in the
same group have the same semantics. The results of our experiments with a large set of
Web query result pages in di®erent domains show that our proposed approaches are highly
e®ective.

Motivating Young Students to Start a Career in Nuclear: The Basic Course of Science and Nuclear Technology, An Educational Activity of Spanish Young Generation in Nuclear (JÓVENES NUCLEARES)

The main objective of this course, conducted by Jóvenes Nucleares (Spanish Young Generation in Nuclear, JJNN), a non-profit organization that depends on the Spanish Nuclear Society (SNE) is to pass on basic knowledge about Science and Nuclear Technology to the general public, mostly students and introduce them to its most relevant points. The purposes of this course are to provide general information, to answer the most common questions about Nuclear Energy and to motivate the young students to start a career in nuclear. Therefore, it is directed mainly to high school and university students, but also to general people that wants to learn about the key issues of such an important matter in our society. Anybody could attend the course, as no specific scientific education is required. The course is done at least once a year, during the Annual Meeting of the Spanish Nuclear Society, which takes place in a different Spanish city each time. The course is done also to whichever university or institution that asks for it to JJNN, with the only limit of the presenter´s availability. The course is divided into the following chapters: Physical nuclear and radiation principles, Nuclear power plants, Nuclear safety, Nuclear fuel, Radioactive waste, Decommission of nuclear facilities, Future nuclear power plants, Other uses of nuclear technology, Nuclear energy, climate change and sustainable development. The course is divided into 15 minutes lessons on the above topics, imparted by young professionals, experts in the field that belongs either to the Spanish Young Generation in Nuclear, either to companies and institutions related with nuclear energy. At the end of the course, a 200 pages book with the contents of the course is handed to every member of the audience. This book is also distributed in other course editions at high schools and universities in order to promote the scientific dissemination of the Nuclear Technology. As an extra motivation, JJNN delivers a course certificate to the assistants. At the end of the last edition course, in Santiago de Compostela, the assistants were asked to provide a feedback about it. Some really interesting lessons were learned, that will be very useful to improve next editions of the course. As a general conclusion of the courses it can be said that many of the students that have assisted to the course have increased their motivation in the nuclear field, and hopefully it will help the young talents to choose the nuclear field to develop their careers

Zhitīe Start͡sa Serafima, sarovskoĭ obiteli īeromonakha, pustynnozhiteli͡a i zatvornika : s izobrazhenīem ott͡sa Serafima i snimkom ego pocherka.

Includes facsimile of "Otkrytīe moshcheĭ i proslavlenīe sv. prepodobnago Serafima, sarovskago chudotvort͡sa, v prisutstvīi Ikh Imperatorskikh Velichestv v īi͡uli͡e 1903 goda." between pages 244 and 245.

Connecting social media to e-commerce:cold-start product recommendation using microblogging information

In recent years, the boundaries between e-commerce and social networking have become increasingly blurred. Many e-commerce websites support the mechanism of social login where users can sign on the websites using their social network identities such as their Facebook or Twitter accounts. Users can also post their newly purchased products on microblogs with links to the e-commerce product web pages. In this paper, we propose a novel solution for cross-site cold-start product recommendation, which aims to recommend products from e-commerce websites to users at social networking sites in 'cold-start' situations, a problem which has rarely been explored before. A major challenge is how to leverage knowledge extracted from social networking sites for cross-site cold-start product recommendation. We propose to use the linked users across social networking sites and e-commerce websites (users who have social networking accounts and have made purchases on e-commerce websites) as a bridge to map users' social networking features to another feature representation for product recommendation. In specific, we propose learning both users' and products' feature representations (called user embeddings and product embeddings, respectively) from data collected from e-commerce websites using recurrent neural networks and then apply a modified gradient boosting trees method to transform users' social networking features into user embeddings. We then develop a feature-based matrix factorization approach which can leverage the learnt user embeddings for cold-start product recommendation. Experimental results on a large dataset constructed from the largest Chinese microblogging service Sina Weibo and the largest Chinese B2C e-commerce website JingDong have shown the effectiveness of our proposed framework.