Tool to Retrieve Less-Filtered Information from the Internet

While users benefit greatly from the latest communication technology, with popular platforms such as social networking services including Facebook or search engines such as Google, scientists warn of the effects of a filter bubble at this time. A solution to escape from filtered information is urgently needed. We implement an approach based on the mechanism of a metasearch engine to present less-filtered information to users. We develop a practical application named MosaicSearch to select search results from diversified categories of sources collected from multiple search engines. To determine the power of MosaicSearch, we conduct an evaluation to assess retrieval quality. According to the results, MosaicSearch is more intelligent compared to other general-purpose search engines: it generates a smaller number of links while providing users with almost the same amount of objective information. Our approach contributes to transparent information retrieval. This application helps users play a main role in choosing the information they consume.

Antonio Chamorro Daza (1903-2003): Scientific production during the first years of exile (1937-1945)

Objetives: The main aim of our work is to highlight the importance of Chamorro’s discoveries in the fields of endocrinology, gynaecology, reproduction and oncology. Methods: Evaluation of the bibliometric impact of Chamorro’s work using Google Scholar, PubMed, Scopus and Gallica. Results: Between 1937 and 1945 Chamorro carried out important research work, the results of which provided valuable discoveries about the activity of the main endocrine glands. Discussion: To achieve our objective, Google Scholar has been the most profitable metasearch engine from a practical point of view, having recognized almost all of the articles published by Chamorro, although having ignored some of them it could be thought that the overall impact of this author is higher than that which has been found. Conclusions: These findings were reflected in the most prestigious journals and widely disseminated in U.S. research institutions, amongst others. In Spain, however, they were ignored. Maybe it’s time to spread it.

Click and sales prediction for OTAs’ digital advertisements: Fuzzy clustering based approach

Within the most productive route, online travel agencies (OTAs) intend to use advanced digital media ads to expand their piece of the industry as a whole. The metasearch engine platforms are among the most consistently used digital media environments by OTAs. Most OTAs offer day by day deals in metasearch engine platforms that are paying per click for each hotel to get reservations. The administration of offering methodologies is critical along these lines to reduce costs and increase revenue for online travel agencies. In this study, we tried to predict both the number of impressions and the regular Click-Through-Rate (CTR) level of hotel advertising for each hotel and the daily sales amount. A significant commitment of our research is to use an extended dataset generated by integrating the most informative features implemented in various related studies as the rolling average for a different amount of day and shifted values for use in the proposed test stage for CTR, impression and sales prediction. The data is created in this study by one of Turkey’s largest OTA, and we are giving OTA’s a genuine application. The results at each prediction stage show that enriching the training data with the OTA-specific additional features, which are the most insightful and sliding window techniques, improves the prediction models ’ generalization capability, and tree-based boosting algorithms carry out the greatest results on this problem. Clustering the dataset according to its specifications also improves the results of the predictions.

Ontology-Based Metasearch Engine in Electronics Area

Paper The goal of search engines is to return accurate and complete results. Satisfying concrete user information needs becomes more and more difficult because of inability in it complete explicit specification and short comes of keyword-based searching and indexing. General search engines have indexed millions of web resources and often return thousands of results to the user query (most of them often inadequate). To increase result’s precession, users sometimes choose search engines, specialized in searching concrete domain, personalized or semantic search. A grand variety of specialized search engines may be found (and used) in the internet, but no one may guarantee finding of existing in the web and needed for the concrete user resources. In this paper we present our research on building a meta-search engine that uses domain and user profile ontologies, as well as information (or metadata), directly extracted from web sites to improve search result quality. We state main requirements to the search engine for students, PHD students and scientists, propose a conceptual model and discuss approaches of it practical realization. Our prototype metasearch engine first perform interactive semantic query refinement and then, using refined query, it automatically generate several search queries, sends them to different digital libraries and web search engines, augments and ranks returned results, using ontologically represented domain and user metadata. For testing our model, we develop domain ontologies in the electronic domain. We will use ontological terminology representation to propose recommendations for query disambiguation, and to ensure knowledge for reranking the returned results. We also present some partial initial implementations query disambiguation strategies and testing results.

User documentation and training at Belle II*

Belle II is a rapidly growing collaboration with members from one hundred and nineteen institutes spread around the globe. The software development team of the experiment, as well as the software users, are very much decentralised. Together with the active development of the software, such decentralisation makes the adoption of the latest software releases by users an essential, but quite challenging task. To ensure the relevance of the documentation, we adopted the policy of in-code documentation and configured a website that allows us to tie the documentation to given releases. To prevent tutorials from becoming outdated, we covered them by unit-tests. For the user support, we use a question and answer service that not only reduces repetition of the same questions but also turned out to be a place for discussions among the experts. A prototype of a metasearch engine for the different sources of documentation has been developed. For training of the new users, we organise centralised StarterKit workshops attached to the collaboration meetings. The materials of the workshops are later used for self-education and organisation of local training sessions.

Utilization of Co-occurrence Pattern Mining with Optimal Fuzzy Classifier for Web Page Personalization

Several users use metasearch engines directly or indirectly to access and gather data from more than one data source. The effectiveness of a metasearch engine is majorly determined by the quality of the results it returns in response to user queries. The rank aggregation methods that have been proposed until now exploit a very limited set of parameters, such as total number of used resources and the rankings they achieved from each individual resource. In this paper, we use the fuzzy-bat to merge the score computation module effectively. Initially, we give a query to different search engines we use and the topnlist from each search engine is chosen for further processing our technique. We then merge the topnlist based on unique links, and we do some parameter calculations such as title-based calculation, snippet-based calculation, content-based calculation, address-based calculation, link-based calculation, uniform resource locator-based calculation, and co-occurrence-based calculation. We give the solutions of the calculations with the user given the ranking of links to the fuzzy-bat to train the system. The system then ranks and merges the links we obtain from different search engines for the query we give.