Activity-driven content adaptation for effective video summarisation

In this paper, we present a novel method for content adaptation and video summarization fully implemented in compressed-domain. Firstly, summarization of generic videos is modeled as the process of extracted human objects under various activities/events. Accordingly, frames are classified into five categories via fuzzy decision including shot changes (cut and gradual transitions), motion activities (camera motion and object motion) and others by using two inter-frame measurements. Secondly, human objects are detected using Haar-like features. With the detected human objects and attained frame categories, activity levels for each frame are determined to adapt with video contents. Continuous frames belonging to same category are grouped to form one activity entry as content of interest (COI) which will convert the original video into a series of activities. An overall adjustable quota is used to control the size of generated summarization for efficient streaming purpose. Upon this quota, the frames selected for summarization are determined by evenly sampling the accumulated activity levels for content adaptation. Quantitative evaluations have proved the effectiveness and efficiency of our proposed approach, which provides a more flexible and general solution for this topic as domain-specific tasks such as accurate recognition of objects can be avoided

A CONCEPTUAL MODEL TO PROTECT BRAND REPUTATION FACING ” FAKE NEWS”

openThis study aims to explore the nature and impact of Fake News on brands and their customers, identify different categories of Fake News, and propose a conceptual model for companies to protect their brand reputation and mitigate the effects of Fake News. The research focuses on the managerial approaches and methodologies that brands can adopt to counter Fake News and safeguard their reputation in the digital era. A comprehensive literature review examines existing studies on Fake News, brand management, and the impact of Fake News on brands and consumers, highlighting the gaps in the literature. The review defines and categorizes Fake News, explores techniques for detecting and mitigating it, and investigates the relationship between Fake News and brand management. The findings reveal a lack of research on the managerial strategies for brands to tackle Fake News effectively. The study emphasizes the importance of developing proactive measures to detect and counter Fake News, as well as building resilience against Fake News attacks. By addressing these gaps, the study aims to contribute to the development of effective strategies for brands to navigate the challenges posed by Fake News in the digital media landscape.This study aims to explore the nature and impact of Fake News on brands and their customers, identify different categories of Fake News, and propose a conceptual model for companies to protect their brand reputation and mitigate the effects of Fake News. The research focuses on the managerial approaches and methodologies that brands can adopt to counter Fake News and safeguard their reputation in the digital era. A comprehensive literature review examines existing studies on Fake News, brand management, and the impact of Fake News on brands and consumers, highlighting the gaps in the literature. The review defines and categorizes Fake News, explores techniques for detecting and mitigating it, and investigates the relationship between Fake News and brand management. The findings reveal a lack of research on the managerial strategies for brands to tackle Fake News effectively. The study emphasizes the importance of developing proactive measures to detect and counter Fake News, as well as building resilience against Fake News attacks. By addressing these gaps, the study aims to contribute to the development of effective strategies for brands to navigate the challenges posed by Fake News in the digital media landscape

Knowledge management and human trafficking: using conceptual knowledge representation, text analytics and open-source data to combat organized crime

Globalization, the ubiquity of mobile communications and the rise of the web have all expanded the environment in which organized criminal entities are conducting their illicit activities, and as a result the environment that law enforcement agencies have to police. This paper triangulates the capability of open-source data analytics, ontological knowledge representation and the wider notion of knowledge management (KM) in order to provide an effective, interdisciplinary means to combat such threats, thus providing law enforcement agencies (LEA’s) with a foundation of competitive advantage over human trafficking and other organized crime

Natural language processing

Beginning with the basic issues of NLP, this chapter aims to chart the major research activities in this area since the last ARIST Chapter in 1996 (Haas, 1996), including: (i) natural language text processing systems - text summarization, information extraction, information retrieval, etc., including domain-specific applications; (ii) natural language interfaces; (iii) NLP in the context of www and digital libraries ; and (iv) evaluation of NLP systems