Collaborative Learning and Convergence: Library Strategies and Solutions with an Eye on the USG Information Technology Strategic Plan

The article discusses collaborative learning as a library strategy of the University System of Georgia (USG) Information Technology Strategic Plan 2010 and the role of convergence in fostering it. It is stated that collaborative learning enables people to achieve quality learning by way of efficient technologies and its integration with content and environments that will benefit people and institutions. Convergence reportedly refers to the integration of distinct library services that will support the shared purpose of learning

IMPLEMENTING E-LEARNING IN THE ROMANIAN EDUCATIONAL SYSTEM - A PRIORITY IN THE CONTEXT OF EU INTEGRATION

This paper intends to examine the development of e-Learning in Romania and to evaluate the gap between Romania and other members of the European Union (EU). Considering that Romania is part of the EU since 2007, it is imperative to achieve, in the shortest possible time, a real convergence with other member states. This requires finding the most effective ways to accelerate the development and increase the competitiveness. Using extensive IT&C technologies represent such a way, and public services – education, too – are among the development priorities on the agendas of all policies, both nationally and European. Thus, the subject treated in the paper is not only present but also of strategic importance for the immediate future of Romania.e-learning, e-education, IT&C

A Supporting Architecture for Generic Service Integration in IMS Learning Design

De la Fuente Valentin, L., Miao, Y., Pardo, A., & Delgado Kloos, C. (2008). A Supporting Architecture for Generic Service Integration in IMS Learning Design. In P. Dillenbourg & M. Specht (Eds.), Times of Convergence. Technologies Across Learning Contexts - Proceedings of the Third European Conference on Technology Enhanced Learning, EC-TEL 2008 (pp. 467-473). September, 16-19, 2008, Maastricht, The Netherlands: Lecture Notes in Computer Science 5192 Springer 2008, ISBN 978-3-540-87604-5.Learning Design offers the possibility of capturing the process, activities, user organization and resources used in a learning experience. But a wider set of scenarios appear when generic services are considered. Integrating such services in a Unit of Learning is difficult due to the lack of a defined bi-directional protocol for information exchange. In this paper the Generic Service Integration paradigm is presented. It extends the Learning Design specification to use generic services, first at the design stage of a Unit of Learning, and then at the deployment and run times. The framework allows for bi-directional exchange of information between a Unit of Learning and a service. The consequences of the approach are that services can be configured to suit the needs of activities in a learning environment, and a Unit of Learning may adapt its behavior based on the events that took place in any of the used services

How Does Forecasting Affect the Convergence of DRL Techniques in O-RAN Slicing?

The success of immersive applications such as virtual reality (VR) gaming and metaverse services depends on low latency and reliable connectivity. To provide seamless user experiences, the open radio access network (O-RAN) architecture and 6G networks are expected to play a crucial role. RAN slicing, a critical component of the O-RAN paradigm, enables network resources to be allocated based on the needs of immersive services, creating multiple virtual networks on a single physical infrastructure. In the O-RAN literature, deep reinforcement learning (DRL) algorithms are commonly used to optimize resource allocation. However, the practical adoption of DRL in live deployments has been sluggish. This is primarily due to the slow convergence and performance instabilities suffered by the DRL agents both upon initial deployment and when there are significant changes in network conditions. In this paper, we investigate the impact of time series forecasting of traffic demands on the convergence of the DRL-based slicing agents. For that, we conduct an exhaustive experiment that supports multiple services including real VR gaming traffic. We then propose a novel forecasting-aided DRL approach and its respective O-RAN practical deployment workflow to enhance DRL convergence. Our approach shows up to 22.8%, 86.3%, and 300% improvements in the average initial reward value, convergence rate, and number of converged scenarios respectively, enhancing the generalizability of the DRL agents compared with the implemented baselines. The results also indicate that our approach is robust against forecasting errors and that forecasting models do not have to be ideal.Comment: This article has been accepted for presentation in IEEE GLOBECOM 202

{\mu}-DDRL: A QoS-Aware Distributed Deep Reinforcement Learning Technique for Service Offloading in Fog computing Environments

Fog and Edge computing extend cloud services to the proximity of end users, allowing many Internet of Things (IoT) use cases, particularly latency-critical applications. Smart devices, such as traffic and surveillance cameras, often do not have sufficient resources to process computation-intensive and latency-critical services. Hence, the constituent parts of services can be offloaded to nearby Edge/Fog resources for processing and storage. However, making offloading decisions for complex services in highly stochastic and dynamic environments is an important, yet difficult task. Recently, Deep Reinforcement Learning (DRL) has been used in many complex service offloading problems; however, existing techniques are most suitable for centralized environments, and their convergence to the best-suitable solutions is slow. In addition, constituent parts of services often have predefined data dependencies and quality of service constraints, which further intensify the complexity of service offloading. To solve these issues, we propose a distributed DRL technique following the actor-critic architecture based on Asynchronous Proximal Policy Optimization (APPO) to achieve efficient and diverse distributed experience trajectory generation. Also, we employ PPO clipping and V-trace techniques for off-policy correction for faster convergence to the most suitable service offloading solutions. The results obtained demonstrate that our technique converges quickly, offers high scalability and adaptability, and outperforms its counterparts by improving the execution time of heterogeneous services

Empowering health personnel for decentralized health planning in India: The Public Health Resource Network

The Public Health Resource Network is an innovative distance-learning course in training, motivating, empowering and building a network of health personnel from government and civil society groups. Its aim is to build human resource capacity for strengthening decentralized health planning, especially at the district level, to improve accountability of health systems, elicit community participation for health, ensure equitable and accessible health facilities and to bring about convergence in programmes and services

Converging Paths to Common Ground: A Multidisciplinary Approach to Influencing Institution Business

Modern academic libraries tend to provide services beyond traditional lending, reference, and enquiry services. Many are converged with other professional or student-focused services such as IT, student services, academic support, or such learning resources as multimedia or print services -- often co-located in space and management structure. At its optimum, this convergence can foster cross-institution working and enable Library services visibility in institution strategy alongside other business continuity services (e.g., student records, Finance, HR). Through the prism of the McKinsey 7S framework, this article reflects on the convergence of Library, IT, Multimedia Services including classroom management, and Print Services at SOAS University of London and examines the process of bringing together staff with varying professional identities and grades to work as a cohesive team delivering front-line, customer-focused services. The article also reflects on how taking a multidisciplinary approach to providing institution support enabled the Customer Services and Operations (CSOps) team to influence institution strategy on space development for learning, teaching, and research support