Collaborative Environments. Considerations Concerning Some Collaborative Systems

It is obvious, that all collaborative environments (workgroups, communities of practice, collaborative enterprises) are based on knowledge and between collaboration and knowledge management there is a strong interdependence. The evolution of information systems in these collaborative environments led to the sudden necessity to adopt, for maintaining the virtual activities and processes, the latest technologies/systems, which are capable to support integrated collaboration in business services. In these environments, portal-based IT platforms will integrate multi-agent collaborative systems, collaborative tools, different enterprise applications and other useful information systems.collaboration, collaborative environments, knowledge management, collaborative systems, portals, knowledge portals, agile development of portals

About the Generalized Reasoning Methods and their Use in Semiotic Systems

In computational semiotics the problem is to emulate a semiosis cycle within a digital computer. This needs the construction of intelligent systems, able to perform intelligent behavior, such as sensorial perception, world modeling, value judgement and behavior generation. These intelligent systems could be generally implemented through object networks and the basic functions mentioned above could be obtained by generalization of some elementary knowledge operators. Based on the three main reasoning methods, deduction, induction and abduction, well known in the philosophy of science and used in AI systems, there were three knew knowledge operators defined: knowledge extraction, knowledge generation and knowledge generation, operators that could be viewed as generalized interpretations of the standard reasoning procedures. This paper presents these new concepts and their connection, the current understanding of generalized deduction, induction and abduction and also how these operators could serve as the building blocks of universal intelligent systems.Semiotic System, Knowledge Units, Deduction, Induction, Abduction

Collaborative Environments. Considerations Concerning Some Collaborative Systems

It is obvious, that all collaborative environments (workgroups, communities of practice, collaborative enterprises) are based on knowledge and between collaboration and knowledge management there is a strong interdependence. The evolution of information systems in these collaborative environments led to the sudden necessity to adopt, for maintaining the virtual activities and processes, the latest technologies/systems, which are capable to support integrated collaboration in business services. In these environments, portal-based IT platforms will integrate multi-agent collaborative systems, collaborative tools, different enterprise applications and other useful information systems

An innovative machine learning-based scheduling solution for improving live UHD video streaming quality in highly dynamic network environments

The latest advances in terms of network technologies open up new opportunities for high-end applications, including using the next generation video streaming technologies. As mobile devices become more affordable and powerful, an increasing range of rich media applications could offer a highly realistic and immersive experience to mobile users. However, this comes at the cost of very stringent Quality of Service (QoS) requirements, putting significant pressure on the underlying networks. In order to accommodate these new rich media applications and overcome their associated challenges, this paper proposes an innovative Machine Learning-based scheduling solution which supports increased quality for live omnidirectional (360◦) video streaming. The proposed solution is deployed in a highly dy-namic Unmanned Aerial Vehicle (UAV)-based environment to support immersive live omnidirectional video streaming to mobile users. The effectiveness of the proposed method is demonstrated through simulations and compared against three state-of-the-art scheduling solutions, such as: Static Prioritization (SP), Required Activity Detection Scheduler (RADS) and Frame Level Scheduler (FLS). The results show that the proposed solution outperforms the other schemes involved in terms of PSNR, throughput and packet loss rate

A machine learning resource allocation solution to improve video quality in remote education

The current global pandemic crisis has unquestionably disrupted the higher education sector, forcing educational institutions to rapidly embrace technology-enhanced learning. However, the COVID-19 containment measures that forced people to work or stay at home, have determined a significant increase in the Internet traffic that puts tremendous pressure on the underlying network infrastructure. This affects negatively content delivery and consequently user perceived quality, especially for video-based services. Focusing on this problem, this paper proposes a machine learning-based resource allocation solution that improves the quality of video services for increased number of viewers. The solution is deployed and tested in an educational context, demonstrating its benefit in terms of major quality of service parameters for various video content, in comparison with existing state of the art. Moreover, a discussion on how the technology is helping to mitigate the effects of massively increasing internet traffic on the video quality in an educational context is also presented

5MART: A 5G SMART scheduling framework for optimizing QoS through reinforcement learning

The massive growth in mobile data traffic and the heterogeneity and stringency of Quality of Service (QoS) requirements of various applications have put significant pressure on the underlying network infrastructure and represent an important challenge even for the very anticipated 5G networks. In this context, the solution is to employ smart Radio Resource Management (RRM) in general and innovative packet scheduling in particular in order to offer high flexibility and cope with both current and upcoming QoS challenges. Given the increasing demand for bandwidth-hungry applications, conventional scheduling strategies face significant problems in meeting the heterogeneous QoS requirements of various application classes under dynamic network conditions. This paper proposes 5MART, a 5G smart scheduling framework that manages the QoS provisioning for heterogeneous traffic. Reinforcement learning and neural networks are jointly used to find the most suitable scheduling decisions based on current networking conditions. Simulation results show that the proposed 5MART framework can achieve up to 50% improvement in terms of time fraction (in sub-frames) when the heterogeneous QoS constraints are met with respect to other state-of-the-art scheduling solutions