Responsible media technology and AI: challenges and research directions

The last two decades have witnessed major disruptions to the traditional media industry as a result of technological breakthroughs. New opportunities and challenges continue to arise, most recently as a result of the rapid advance and adoption of artificial intelligence technologies. On the one hand, the broad adoption of these technologies may introduce new opportunities for diversifying media offerings, fighting disinformation, and advancing data-driven journalism. On the other hand, techniques such as algorithmic content selection and user personalization can introduce risks and societal threats. The challenge of balancing these opportunities and benefits against their potential for negative impacts underscores the need for more research in responsible media technology. In this paper, we first describe the major challenges—both for societies and the media industry—that come with modern media technology. We then outline various places in the media production and dissemination chain, where research gaps exist, where better technical approaches are needed, and where technology must be designed in a way that can effectively support responsible editorial processes and principles. We argue that a comprehensive approach to research in responsible media technology, leveraging an interdisciplinary approach and a close cooperation between the media industry and academic institutions, is urgently needed.

Distance education: the introduction of new computer technologies in the learning process

The article defines the concept of distance multimedia training system and consider the use of distance multimedia training systems in higher education in the field of design; investigated the structure and composition of distance learning multimedia systems; identify the specific features of learning using distance multimedia learning systems; the specifics of user personalization in the multimedia space are considered; a format for describing data in distance multimedia learning systems has been developed. The results show that the automatic evaluation system for graphic images is an important step towards the creation of distance learning systems in the field of graphic design.

Profiling and Personalization in Internet of Things Environments

The chapter considers problems of user personalization and resources competence modeling in the internet of things (IoT) environments. Creation of the user profiles and its utilization during the interaction of the user with IoT resources significantly increase the efficiency of such interaction. When the user generates a task to perform by the IoT resources, the formal model of this task is expanded by the relevant information in accordance with the user profile model. The obtained results should be presented to the user in accordance to his/her preferences from the user profile model. Resource competence profile should store information about the resource competencies and constraints that have to be satisfied to enable these competences. In this case, resource competence profiles automate their interaction in IoT environments.

Analyzing and Predicting User Navigation Pattern from Weblogs using Modified Classification Algorithm

<p>A Weblogs contains the history of User Navigation Pattern while user accessing the websites. The user navigation pattern can be analyzed based on the previous user navigation that is stored in weblog. The weblog comprises of various entries like IP address, status code and number of bytes transferred, categories and time stamp. The user interest can be classified based on categories and attributes and it is helpful in identifying user behavior. The aim of the research is to identifying the interested user behavior and not interested user behavior based on classification. The process of identifying user interest, it consists of Modified Span Algorithm and Personalization Algorithm based on the classification algorithm user prediction can be analyzed. The research work explores to analyze user prediction behavior based on user personalization that is captured from weblogs. </p>