Människan är beroende av de ekosystemtjänster som naturen bidrar med, i staden är människan inte alltid medveten om dessa ekosystemtjänster och kan lätt förbise dessa. Träden i städerna förser stadsborna med fler ekosystemtjänster än de är medvetna om. Ekosystemtjänster som temperaturreglering och dagvattenhantering kan bli kostsamma att återskapa om alla naturliga element som bidrar med dessa helt gratis är borta. För att inte missa värdefulla naturmiljöer och därmed förlora pengar i planerad exploatering har ett verktyg för att ranka ekosystemtjänster kopplade till urbana träd tagits fram. För att på ett tydligt sätt kunna peka ut olika ekosystemtjänster i staden har en värderingsmatris för ranking av dessa skapats. Rankingvärdet en ekosystemtjänst inom ett område fått kan därpå multipliceras med egen inputdata, en egen värdering av ekosystemtjänsten. Resultatet av sammanställningen visas i form av kartbilder med graderade polygoner för att synliggöra vart ekosystemtjänsten finns i störst redundans. Så vitt författaren känner till, finns det ingen annan typ av kartläggning som kombinerar möjligheten med litteraturbaserad värdering och egen åsikt. Verktyget kan användas i två delar, den litteraturbaserade värderingsmatrisen kan användas enskilt och genererar ett resultat om vart det finns högst värde för ekosystemtjänster, eller gemensamt med egen input kan ett resultat visas i GIS. Metoden är unik och kan förhoppningsvis avvärja konflikter i t ex. planarbeten då ett visst område kan bli extra viktigt, även om olika ekosystemtjänster fått högst värde av de som använder verktyget. Olika åsikter om ekosystemtjänsters värdekan då ändå generera samma resultat.

The use of social media is expanding significantly and can serve a variety of purposes. Over the last few years, users of social media have played an increasing role in the dissemination of emergency and disaster information. It is becoming more common for affected populations and other stakeholders to turn to Twitter to gather information about a crisis when decisions need to be made, and action is taken. However, social media platforms, especially on Twitter, presents some drawbacks when it comes to gathering information during disasters. These drawbacks include information overload, messages are written in an informal format, the presence of noise and irrelevant information. These factors make gathering accurate information online very challenging and confusing, which in turn may affect public, communities, and organizations to prepare for, respond to, and recover from disasters. To address these challenges, we present an integrated three parts (clustering-classification-ranking) framework, which helps users choose through the masses of Twitter data to find useful information. In the first part, we build standard machine learning models to automatically extract and identify topics present in a text and to derive hidden patterns exhibited by a dataset. Next part, we developed a binary and multi-class classification model of Twitter data to categorize each tweet as relevant or irrelevant and to further classify relevant tweets into four types of community engagement: reporting information, expressing negative engagement, expressing positive engagement, and asking for information. In the third part, we propose a binary classification model to categorize the collected tweets into high or low priority tweets. We present an evaluation of the effectiveness of detecting events using a variety of features derived from Twitter posts, namely: textual content, term frequency-inverse document frequency, Linguistic, sentiment, psychometric, temporal, and spatial. Our framework also provides insights for researchers and developers to build more robust socio-technical disasters for identifying types of online community engagement and ranking high-priority tweets in disaster situations.

Search Engine Optimization (SEO) is the technique of improving Web sites visibility in search engines. Since the algorithms that search engines are based on become more intelligent each day, there is a constant urge for new knowledge. In collaboration with RankTrail, new research for discovering insights about SEO has been conducted. Hypotheses around alleged ranking factors have been created based on qualitative interviews. Through a quantitative case study these hypotheses have been analyzed. The first part of the analysis consisted of calculating the Spearman’s Rank-Order Correlation. Secondly, these correlations has been visualised using histograms. Additional statistical tests have been performed. Number of images, use of HTTPS and use of a custom meta-description stand out amongst all factors analyzed. All three have a higher mean, but also a higher effect size calculated from Cohen’s d. However, the results of this study show that none of the factors indicate a strong impact on SEO.

No description available.

The thesis describes a search engine of Google company and its method of web pages evaluating, which is used for sorting them on the search engine result page. In addition to the description of how search engines work in general, the thesis targets on differencies between Google and other search engines, as well as it mentions characteristics and principles designed by its founders, Larry Page and Sergey Brin, which make it unique. In the second part there are outlined some of many signals which are rated for every single web page. The most concerned signal is PageRank as the leading idea of pages evaluating, based on their link scheme which is founded on principles of citation analysis. Factors for evaluating are analysed using particular examples, and the topic is ended by the description of Google search engines changes from its beginning in 1998 until present.

A multicriteria approach to assess the ranking performance of Portuguese companies is utilized. In the process, the research methodology which utilizes ELECTRE III. a fuzzy set theory based procedure, and the SFR Software is illustrated using a conceptual research framework. The results derived from the application presented in this study, are used to propose a set of economical and financial benchmarks indicators to guide Portuguese managers in pursuing a strategy of excellence.

No description available.

Le classement des objets est une des questions importantes et typiques dans notre vie quotidienne. De nombreuses applications ont besoin de classifier des objets en fonction de certains critères, parfois simple comme de classifier les étudiants dans une classe en fonction de relevé de notes ou plus compliqué comme le classement des universités. Classifier des objets consiste à les ordonner selon certains critères exigés par une application spécifique.Avec la popularisation de l’Internet, un problème typique qui a émergé des deux dernières décennies est le classement des résultats renvoyés par les moteurs de recherche. Dans les moteurs de recherche classiques (comme Google, Yahoo ou Bing ),l’importance d’une page web est la base pour le classement. Cette valeur est calculée sur la base de l’analyse des hyper-liens entre les pages Web. Avec un ensemble de documents V={v1, ..., vn}, quand il y a une requête q d’un utilisateur arrivant, le moteur de recherche cherche des documents dans V correspondant à la requête q, puis trie les documents dans l’ordre décroissant de leur pertinence pour la requête. Ce processus peut être réalisé grâce à une fonction de classement qui permet de cal culer la similarité sim(q, vi) entre la requête q et un document vi ∈ V. La fonction de classement peut être considérée comme le noyau qui détermine essentiellement la qualité du moteur de recherche. / Ranking objects is one of the important and typical issues in our daily life. Many applications need to rank objects according to certain criteria, as simple as ranking students in a class according to average grades, or more complicated as ranking universities. Ranking objects means to arrange them in accordance with some criteria depending on the specific application.In the era of the Internet, a typical problem emerging in the last decades is the ranking of results returned by search engines. In conventional search engines (like Google, Yahoo or Bing ), the importance of a web page is the basis for ranking. This value is determined based on the analysis of graph links between web pages. With a set of documents V={v1, ..., vn}, when there is a user’s query q arriving, the search engine looks for documents in V matching the query q, then sorts the documents according to their relevance to the query in descending order. This process can be done thanks to a ranking function which allows us to compute the similarity s(q,vi) between the query q and a document vi ∈ V . Obviously, the ranking function can be seen as the core and significantly determines the quality of the search engine.

Indiana University-Purdue University Indianapolis (IUPUI) / With the large number of Android apps available in app stores such as Google Play, it has become increasingly challenging to choose among the apps. The users generally select the apps based on the ratings and reviews of other users, or the recommendations from the app store. But it is very important to take the security into consideration while choosing an app with the increasing security and privacy concerns with mobile apps. This thesis proposes different ranking schemes for Android apps based on security apps evaluated from the static code analysis tools that are available. It proposes the ranking schemes based on the categories of evidences reported by the tools, based on the frequency of each category, and based on the severity of each evidence. The evidences are gathered, and rankings are generated based on the theory of Subjective Logic. In addition to these ranking schemes, the tools are themselves evaluated against the Ghera benchmark. Finally, this work proposes two additional schemes to combine the evidences from difference tools to provide a combined ranking.

Severe weather outbreaks are fairly common events that occur multiple times a year. Many studies have attempted to define and quantify these outbreaks, however, no work has been done to directly relate synoptic-scale processes to outbreak intensity using the N15 ranking index. It is believed that a statistically significantly strong relationship between outbreak severity and quantified synoptic-scale parameters exists and can be utilized to predict the severity of an upcoming outbreak using the N15 ranking index. Utilizing the NCEP-NCAR Reanalysis dataset, synoptic-scale variables were chosen and standardized into domains created from areal coverages. A series of tests were completed, including stepwise regression, principal component analysis, and a bootstrap cross-validation method to find the most significant variables and best domain size. The findings from this study suggest that synoptic-scale processes do not have a strong relationship to severe weather outbreak intensity and that future work would be necessary.