A influência das animações sobre o que é e quem faz ciência

The biblical story of Samson, which the recognised 'Introductions to the Old Testament' rubricate under the somewhat sparse genre-labels of 'heroic narrative', 'heroic saga' or 'folk tale', is irrespective of its episodic structure and some inner tensions an excellent plot for the movies. Therefore, it was far from surprising that Samson made his way quite early onto movie screens. He is arguably the first figure of the Old Testament on celluloid. Once Samson was established as a movie-hero he experienced numerous remakes. The encyclopaedic 'Internet Movie Database' has more than sixty movies registered featuring a 'Samson'. As before with Voltaire, Cecil B. DeMille presents Delilah as a passionately loving woman, who desires Samson sexually and whose hatred towards him is just the reverse of her profound love. At the end all movies, which take reference to Judg 13-16, tend to a harmonization and a happy ending. Keywords: biblical story; Cecil B. DeMille; Delilah; internet movie database; movies; Old Testament; Samson

Background Aflatoxin B1 (AFB1) a mycotoxin produced by Aspergillus flavus and A. parasiticus is a potent carcinogen and causative agent of hepatocellular carcinoma (HCC). It is a food contaminant which presents a major risk to human health. AFB1 contamination poses a significant economic burden, as 25% of the world's food crops need to be destroyed annually. The mechanism of action (MOA) of aflatoxins remains to be fully elucidated. Recent findings suggest that AFB1 mediated endocrine disruption may occur in the population of regions with high contamination, even without evidence of direct dietary intake. Objective An integrative systems biology approach was undertaken to decipher the estrogenic component of the mechanism of action (MOA) of AFB1. Methods Molecular Docking and Molecular dynamics simulations were performed to examine the binding affinity of AFB1 and its metabolite aflatoxin Q1 (AFQ1) with the Estrogen Receptors (ERs). Differential gene expression (DGE), gene ontology (GO) and pathway analyses were carried out on hepatic transcriptomic data generated from in vivo AFB1 exposures. In parallel exposures to the synthetic estrogen ethinylestradiol (EE2) were examined for overlapping effects. Finally, protein-protein interaction (PPI) network analysis assessed the involvement of estrogen responsive targets (ERTs) associated with aflatoxin exposure. The free energies of binding affinity and estimated equilibrium dissociation constants (K D ) demonstrated that AFB1 and AFQ1 can interact with the ERα and ERβ. DGE and GO analyses highlighted overlap in the responses between AFB1 and EE2 treatments with the activation of key processes involved in estrogenic signaling. PPI network analyses after AFBI exposure revealed a dynamic response to AFB1 treatments with the solid involvement of ERTs in regulatory networks. Conclusions This study revealed molecular interactions between aflatoxins (AFB1, AFQ1) and ERs in addition to overlap in differentially expressed genes and biological processes following AFB1 and EE2 exposures. The estrogenic components at the core of the PPI networks suggest that ER-mediated signaling pathways are a major component in the MOA of aflatoxins. Aflatoxin B1 (AFB1) • Mechanism of action (MOA) • Ethinylestradiol (EE2) • Protein-protein interaction (PPI) network analysis • Estrogen receptors • ERα and Erβ • Estrogen responsive targets (ERTs)

This research studies the development of a new prototype of visualization in support of movie retrieval. The goal of information visualization is unveiling of large amounts of data or abstract data set using visual presentation. With this knowledge the main goal is to develop a 2D presentation of information on movies from the IMDB (Internet Movie Database) as our movie search engine. The aim is to amplify the perception of users over the retrieval environment while preserving the output quality at an acceptable meaningful level. Visualization ideas deal with visualization of information seeking results. These visualization techniques have been collected from the literature on how to map the results of the information retrieval process. We propose a 2D visual interface for mapping collections of movies and exploration of their related information to maximize density of needed information in a single page. For this purpose, we introduce a movie categorization scheme to help users in navigating through the movies information. The traditional style (interface) of clicking a link to view details of documents in most of the search engines like the IMDB (Internet Movie Database) as our movie search engine to find information is very uninteresting and tedious. This is because when clicking a particular link the user’s focus is shifted to the new page, and if the information presented is not to their interest, they will need to switch back to the movie’s search results. The key contribution is thus a reasonable mapping result of a query on an actor/actress movie database displayed in just one page that can amplify visual perception of retrieved movies. This theory of data graphics interface focuses on maximization of the density of useful pertinent information with respect to users query in a screen page. This method of grouping of information needs some data extraction algorithms by parsing and crawling the IMDB web pages that are useful to retrieve important movies information. Our graphical-based visualization provides a correct understanding of information that users can view information without reading them. A qualitative experimental test comparing the classic (traditional) interface of the IMDB and visual interface was conducted.

Judgement devices and the evaluation of singularities: The use of performance ratings and narrative information to guide film viewer choice

Advances in Web 2.0 technology encourage the creation of personal website content involving sentiments such as blogs, tweets, web forums, and various types of social media. The Internet Movie Database (IMDb) is a website that provides information about films from around the world, including the people involved, nominations received, and reviews from visitors. The number of movies and reviews on IMDb causes users or visitors to check the reviews to find out the film rating, so it takes time for users who have no experience using IMDb. Sentiment analysis can be a solution to label positive and negative reviews. One of the algorithms used in sentiment analysis is the Support Vector Machine (SVM) algorithm. This study aimed to test the accuracy of the SVM algorithm in the classification of sentiment review films on IMDb. The tests carried out using the Support Vector Machine algorithm resulted in an accuracy value of 86.5%. The SVM algorithm can also produce a precision value of 90.67% and a recall value of 91.62%.

Online review forums provide consumers with essential information about goods and services by facilitating word-of-mouth communication. Despite that preferences are correlated to demographic characteristics, reviewer gender is not often provided on user profiles. We consider the case of the internet movie database (IMDb), where users exchange views on movies. Like many forums, IMDb employs collaborative filtering such that by default, reviews are ranked by perceived utility. IMDb also provides a unique gender filter that displays an equal number of reviews authored by men and women. Using logistic classification, we compare reviews with respect to writing style, content and metadata features. We find salient differences in stylistic features and content between reviews written by men and women, as predicted by sociolinguistic theory. However, utility is the best predictor of gender, with women’s reviews perceived as being much less useful than those written by men. While we cannot observe who votes at IMDb, we do find that highly rated female-authored reviews exhibit “male” characteristics. Our results have implications for which contributions are likely to be seen, and to what extent participants get a balanced view as to “what others think” about an item.

Along with the development of technology, the film industry continues to increase, this can be seen from the number of films that appear both in cinemas and tv shows. The Internet Movie Database (IMDb) is a website that provides information about films from around the world, including the people involved in the films. Information contained on IMDB such as actor/actress, director, writer, to the soundtrack used. In addition, IMDb is the most popular and trusted source of information for movies, TV, and other celebrity content. In this case, the researcher will conduct research on the film with what title is the most popular among the public by looking at some of the parameters contained in IMDB such as the number on the rating, score, certificate, and votes obtained from the audience. The data used comes from the Kaggle.com website. The data mining method used is the K-Means clustering method. To find out the optimal cluster value, the Davies Bouldin index is used. The K-Means algorithm will group the data based on the centroid. The parameters used for clustering are runtime, IMDB rating, meta score, number of votes, and gross. The results of the study obtained that the average calculation of the highest attributes was 48.74 and the number of clusters formed was 4 clusters. The results of the evaluation using the confusion matrix obtained an accuracy value of 100%.

O tempora! O mores! Cultural modernisation and nudity depiction in European cinema

This research compares the fifty top-rated movies for 2000–2009 recommended by the Internet Movie Database (IMDb) with the holdings of ten medium-sized public libraries. The study shows that there was no relation between a movie's rank in the IMDb listing and the number of copies held. Relationships appeared between number of library holdings and country of production (U.S. productions favored), genre (family, sci-fi, animation, adventure favored), and Motion Picture Association of America (MPAA) rating (G, PG, PG-13 preferred). Library holdings seem to reflect preferences of IMDb raters aged 30 and older, not popular movie tastes.

This study analyzed OCLC Faceted Application of Subject Terminology (FAST) headings and Internet Movie Database (IMDb) “plot keywords” for a shared set of documentary films to determine how well the aboutness identified by the former was reproduced by the latter. A randomized list of documentary films held at the Iowa State University Library, a large academic research library, was created. The FAST headings were compared to each “plot keyword” used in the corresponding IMDb record to determine if they met one or more of the match types defined for this study: exact match, close match, broad match, narrow match, and closely-related match. In total, 604 FAST headings from 100 OCLC records were analyzed. This data was used to calculate breakdowns of average recall and average precision evaluating the IMDb records using the OCLC records. This study found that relatively little of the aboutness expressed by FAST headings was represented by the “plot keywords” in the corresponding IMDb records. The average recall and average precision of the IMDb records, judged as an attempt to reproduce the FAST headings, was 23.38% and 18.89%, respectively. By comparing OCLC and IMDb subject metadata for documentary films, this study advances scholarship on the relationship between professionally-created and user-created metadata.

This study aims to analyze the features of the Internet Movie Database (IMDb) application using the Customer Knowledge Management (CKM) method. CKM is an approach to collecting, managing and leveraging customer knowledge to support decision making and improve customer relationships. In the context of IMDb, CKM can assist in understanding user needs and preferences, providing a better experience, and increasing user satisfaction. This study uses a feature analysis approach to evaluate key features in IMDb applications and determine how these features contribute to management. customer knowledge. Features that will be analyzed include complete information about movies and TV series, cast lists, directors, producers, ratings, user reviews, search features, recommendations, and community features. The method used in this study is CKM, which involves collecting data from different sources, such as IMDb application documentation, analysis of application components, and user feedback. These data are then analyzed and evaluated to determine the effectiveness of IMDb features in supporting customer knowledge management. The results of this analysis are expected to provide useful insights for IMDb application developers to improve user experience, improve existing features, and identify areas for improvement. potential. In addition, this research can also contribute to the understanding of the application of CKM in the context of the entertainment industry and the importance of customer knowledge management in achieving higher user satisfaction.

The number of movies produced in the world is growing at an exponential rate and success rate of movie is of utmost importance since billions of dollars are invested in the making of each of these movies. In such a scenario, prior knowledge about the success or failure of a particular movie and what factor affect the movie success will benefit the production houses since these predictions will give them a fair idea of how to go about with the advertising and campaigning, which itself is an expensive affair altogether. So, the prediction of the success of a movie is very essential to the film industry. In this proposed research, we give our detailed analysis of the Internet Movie Database (IMDb) and predict the IMDb score. This database contains categorical and numerical information such as IMDb score, director, gross, budget and so on and so forth. This research proposes a way to predict how successful a movie will be prior to its arrival at the box office instead of listening to critics and others on whether a movie will be successful or not. The proposed research provides a quite efficient approach to predict IMDb score on IMDb Movie Dataset. We will try to unveil the important factors influencing the score of IMDb Movie Data. We have used different algorithms in the research work for analysis but among all Random forest gave the best prediction accuracy which is better in comparison to the previous studies. In the exploratory analysis we found that number of voted users, number of critics for reviews, number of Facebook likes, duration of the movie and gross collection of movie affect the IMDb score strongly. Drama and Biopic movies are best in genres.

This paper is concerned with the further theoretical development of popular geopolitics with explicit reference to audience dispositions and reception more generally. Using the James Bond film series, and in particular Die Another Day (2002) and the Internet Movie Database (IMDb), it is contended that political geographers need to better understand how audiences and film fans in particular interpret the popular geopolitics of film. One of the striking features of fans who comment about their interest in James Bond on the IMDb is the degree to which these films invite detailed and devoted readings of plotlines, character development, links to contemporary geopolitics and what might be called ‘Bond trivia’. The paper concludes with a call for further investigation into the way in which film is consumed by fans and non‐fans alike, alongside inter alia a more detailed appreciation of the role of on‐line discussion fora such as the IMDb.

오피니언 마이닝과 네트워크 분석을 활용한 상품 커뮤니티 분석: 영화 흥행성과 예측 사례

SummaryThis article promotes Bayesian intuition by introducing the concept of shrinkage with some motivational examples involving basketball statistics of Rudy Gobert and movie rankings from the Internet Movie Database. Supplementary and fully automated R code is provided to allow students to explore shrinkage in this dataset on their own. The content is targeted at the first‐year undergraduate level. Students with prior exposure to R would most benefit from the supplementary R code.