HIERARCHY OF NEEDS IN DAN GILROY’S NIGHTCRAWLER

Nightcrawler is a crime-thriller movie directed by Dan Gilroy. The movie is about an ambitious videographer who sells violent footage of accidents and brutal crimes to the local news TV. The focus of this study is to analyse the motivation of Louis Bloom, the main character in the movie, using Abraham Maslow’s theory of needs. This theory is illustrated in a shape of five layered pyramid which represents every need that must be fulfilled by human. Those needs are physiological needs, safety needs, love or belongingness needs, esteem needs, and self-actualization needs. The result of the discussion shows the five phases of theory of needs reflected in Lou’s life

Symbiosis between the TRECVid benchmark and video libraries at the Netherlands Institute for Sound and Vision

Audiovisual archives are investing in large-scale digitisation efforts of their analogue holdings and, in parallel, ingesting an ever-increasing amount of born- digital files in their digital storage facilities. Digitisation opens up new access paradigms and boosted re-use of audiovisual content. Query-log analyses show the shortcomings of manual annotation, therefore archives are complementing these annotations by developing novel search engines that automatically extract information from both audio and the visual tracks. Over the past few years, the TRECVid benchmark has developed a novel relationship with the Netherlands Institute of Sound and Vision (NISV) which goes beyond the NISV just providing data and use cases to TRECVid. Prototype and demonstrator systems developed as part of TRECVid are set to become a key driver in improving the quality of search engines at the NISV and will ultimately help other audiovisual archives to offer more efficient and more fine-grained access to their collections. This paper reports the experiences of NISV in leveraging the activities of the TRECVid benchmark

Detecting complex events in user-generated video using concept classifiers

Automatic detection of complex events in user-generated videos (UGV) is a challenging task due to its new characteristics differing from broadcast video. In this work, we firstly summarize the new characteristics of UGV, and then explore how to utilize concept classifiers to recognize complex events in UGV content. The method starts from manually selecting a variety of relevant concepts, followed byconstructing classifiers for these concepts. Finally, complex event detectors are learned by using the concatenated probabilistic scores of these concept classifiers as features. Further, we also compare three different fusion operations of probabilistic scores, namely Maximum, Average and Minimum fusion. Experimental results suggest that our method provides promising results. It also shows that Maximum fusion tends to give better performance for most complex events

Crisis Communications: How Businesses Respond in the Wake of Tragedy

Crisis communication is an ever-evolving form of communication that is integral to a business’s success. When tragedy strikes, businesses must have a thorough plan of response that manages the situation and protects their brand. This paper discusses the definition of crisis communication, its history, and how modern trends like social media, have revolutionized it. This study is important because it influences a business’s public perception, and sustainability. Thorough knowledge of crisis communication is critical to a business student’s education and will prepare them for working in fast-paced communication and business environments. An analysis of this topic should yield an understanding of crisis communication and how it can be best applied in crisis situations

An empirical study of inter-concept similarities in multimedia ontologies

Generic concept detection has been a widely studied topic in recent research on multimedia analysis and retrieval, but the issue of how to exploit the structure of a multimedia ontology as well as different inter-concept relations, has not received similar attention. In this paper, we present results from our empirical analysis of different types of similarity among semantic concepts in two multimedia ontologies, LSCOM-Lite and CDVP-206. The results show promise that the proposed methods may be helpful in providing insight into the existing inter-concept relations within an ontology and selecting the most facilitating set of concepts and hierarchical relations. Such an analysis as this can be utilized in various tasks such as building more reliable concept detectors and designing large-scale ontologies