Intelligent synthesis mechanism for deriving streaming priorities of multimedia content

We address the problem of integrating user preferences with network quality of service parameters for the streaming of media content, and suggest protocol stack configurations that satisfy user and technical requirements to the best available degree. Our approach is able to handle inconsistencies between user and networking considerations, formulating the problem of construction of tailor-made protocols as a prioritization problem, solvable using fuzzy programming

Multicriteria decision making for enhanced perception-based multimedia communication

This paper proposes an approach that integrates technical concerns with user perceptual considerations for intelligent decision making in the construction of tailor-made multimedia communication protocols. Thus, the proposed approach, based on multicriteria decision making (MDM), incorporates not only classical networking considerations, but, indeed, user preferences as well. Furthermore, in keeping with the task-dependent nature consistently identified in multimedia scenarios, the suggested communication protocols also take into account the type of multimedia application that they are transporting. Lastly, this approach also opens the possibility for such protocols to dynamically adapt based on a changing operating environment and user's preferences

Newsroom 3.0: Managing Technological and Media Convergence in Contemporary Newsrooms

News consumers are changing their way of accessing and interacting with news content, of which they are now prosumers (combined producers and consumers). Consequently, communication organizations are facing great challenges posed by the decrease of paying readers and the competition imposed by emergent technologies that allow new forms to produce and disseminate news. To understand the role of the journalists and their managers in this challenge, we investigate how top news organizations are tackling this crisis. The results of this research, of a qualitative and exploratory nature, led us to propose a framework - Newsroom 3.0 - of a collaborative environment to support the production of news in an integrated, convergent and cybernetic newsroom. Newsroom 3.0 will provide support to the work of interdisciplinary teams, in respect of the coordination of the activities developed, as well as the cooperative production of content and communication between newsroom professionals and news prosumers

Gas Detection and Identification Using Multimodal Artificial Intelligence Based Sensor Fusion

With the rapid industrialization and technological advancements, innovative engineering technologies which are cost effective, faster and easier to implement are essential. One such area of concern is the rising number of accidents happening due to gas leaks at coal mines, chemical industries, home appliances etc. In this paper we propose a novel approach to detect and identify the gaseous emissions using the multimodal AI fusion techniques. Most of the gases and their fumes are colorless, odorless, and tasteless, thereby challenging our normal human senses. Sensing based on a single sensor may not be accurate, and sensor fusion is essential for robust and reliable detection in several real-world applications. We manually collected 6400 gas samples (1600 samples per class for four classes) using two specific sensors: the 7-semiconductor gas sensors array, and a thermal camera. The early fusion method of multimodal AI, is applied The network architecture consists of a feature extraction module for individual modality, which is then fused using a merged layer followed by a dense layer, which provides a single output for identifying the gas. We obtained the testing accuracy of 96% (for fused model) as opposed to individual model accuracies of 82% (based on Gas Sensor data using LSTM) and 93% (based on thermal images data using CNN model). Results demonstrate that the fusion of multiple sensors and modalities outperforms the outcome of a single sensor.Comment: 14 Pages, 9 Figure

A comparison of reinforcement learning algorithms in fairness-oriented OFDMA schedulers

Due to large-scale control problems in 5G access networks, the complexity of radioresource management is expected to increase significantly. Reinforcement learning is seen as apromising solution that can enable intelligent decision-making and reduce the complexity of differentoptimization problems for radio resource management. The packet scheduler is an importantentity of radio resource management that allocates users’ data packets in the frequency domainaccording to the implemented scheduling rule. In this context, by making use of reinforcementlearning, we could actually determine, in each state, the most suitable scheduling rule to be employedthat could improve the quality of service provisioning. In this paper, we propose a reinforcementlearning-based framework to solve scheduling problems with the main focus on meeting the userfairness requirements. This framework makes use of feed forward neural networks to map momentarystates to proper parameterization decisions for the proportional fair scheduler. The simulation resultsshow that our reinforcement learning framework outperforms the conventional adaptive schedulersoriented on fairness objective. Discussions are also raised to determine the best reinforcement learningalgorithm to be implemented in the proposed framework based on various scheduler settings

Using olfactory media cues in e-learning – perspectives from an empirical investigation

People interact with computers using their senses. Currently, in a digital context, traditional digital media like videos and images used to convey information to users, and these media can be used as a source of information. However, relatively few studies have been conducted on olfactory media as a source of information in a digital context. In this paper, we report on a study that examined the possibility of using olfactory media as a source of information and whether its usage as informational cues enhances learning performance and user Quality of Experience (QoE). To this end, an olfactory-enhanced quiz (web-based) was developed about four countries. The quiz contained different types of questions employing four types of digital media in their contents: text, image, audio and olfactory media. Four scents were used that were considered to be related to the respective countries. Sixty-four participants were invited to our experiment to evaluate this application. Our results revealed that usage of olfactory media synchronised with traditional digital media had a significant impact on learner performance compared to the case when no olfactory media was employed. In respect of user QoE, it was found that olfactory media influenced users positively; moreover, they were passionate about engaging with enhanced olfactory applications in the future

How Do We Experience Crossmodal Correspondent Mulsemedia Content?

Sensory studies emerged as a significant influence upon Human Computer Interaction and traditional multimedia. Mulsemedia is an area that extends multimedia addressing issues of multisensorial response through the combination of at least three media, typically a non-traditional media with traditional audio-visual content. In this paper, we explore the concepts of Quality of Experience and crossmodal correspondences through a case study of different types of mulsemedia setups. The content is designed following principles of crossmodal correspondence between different sensory dimensions and delivered through olfactory, auditory and vibrotactile displays. The Quality of Experience is evaluated through both subjective (questionnaire) and objective means (eye gaze and heart rate). Results show that the auditory experience has an influence on the olfactory sensorial responses and lessens the perception of lingering odor. Heat maps of the eye gazes suggest that the crossmodality between olfactory and visual content leads to an increased visual attention on the factors of the employed crossmodal correspondence (e.g., color, brightness, shape)

Why do commercial companies contribute to open source software?

This is the post-print version of the Article. The official published version can be accessed from the link belowMany researchers have pointed out that the opensource movement is an interesting phenomenon that is difficult to explain with conventional economic theories. However, while there is no shortage on research on individuals’ motivation for contributing to opensource, few have investigated the commercial companies’ motivations for doing the same. A case study was conducted at three different companies from the IT service industry, to investigate three possible drivers: sale of complimentary services, innovation and open sourcing (outsourcing). We offer three conclusions. First, we identified three main drivers for contributing to opensource, which are (a) selling complimentary services, (b) building greater innovative capability and (c) cost reduction through open sourcing to an external community. Second, while previous research has documented that the most important driver is selling complimentary services, we found that this picture is too simple. Our evidence points to a broader set of motivations, in the sense that all our cases exhibit combinations of the three drivers. Finally, our findings suggest that there might be a shift in how commercial companies view opensource software. The companies interviewed have all expressed a moral obligation to contribute to open source