URGENSI KOLABORASI RISET DALAM RANGKA ALIH TEKNOLOGI PENANGGULANGAN COVID-19

The coronavirus respiratory syndrome (COVID-19) pandemic has become a global public health problem. One of the main efforts to overcome this problem is research and development of technology in the health sector. This condition requires the readiness of the State Civil Apparatus (ASN), especially researchers and engineers, to increase the capacity or competence through technology transfer mechanisms in research collaboration schemes, both at the national and international levels. This study aims to find out, understand and explain two things, the urgency of research collaboration in the context of transferring COVID-19 countermeasures technology and strategic efforts to build research collaboration. This study uses a qualitative method with a descriptive analytical approach. The results of the study shows two things, first, that research collaboration in the context of technology transfer is significantly urgent to be implemented in order to encourage researchers and engineers to gain new knowledge about technology in the health sector, and second, strategic efforts that can be implemented by collaborating with researchers and engineers of credible research institutions in the health sector, and have implemented an open science policy which will significantly assist the technology transfer process without being constrained by intellectual property issues

Lifelong Federated Reinforcement Learning: A Learning Architecture for Navigation in Cloud Robotic Systems

This paper was motivated by the problem of how to make robots fuse and transfer their experience so that they can effectively use prior knowledge and quickly adapt to new environments. To address the problem, we present a learning architecture for navigation in cloud robotic systems: Lifelong Federated Reinforcement Learning (LFRL). In the work, We propose a knowledge fusion algorithm for upgrading a shared model deployed on the cloud. Then, effective transfer learning methods in LFRL are introduced. LFRL is consistent with human cognitive science and fits well in cloud robotic systems. Experiments show that LFRL greatly improves the efficiency of reinforcement learning for robot navigation. The cloud robotic system deployment also shows that LFRL is capable of fusing prior knowledge. In addition, we release a cloud robotic navigation-learning website based on LFRL

What works?: the culture of evidence in university teaching

This article analyses the culture of evidence in university teaching and its implications in the professional training of teachers in higher educa tion. The new culture of organisati on and assessment introduced into university teaching has brought about the configuration of a management model geared towards results and accountability based on solid evidence. Its implementation means that both administrators and teachers are asking themselves: what works? This study shows that the implementation of a culture of evidence requires the adoption of a pluralist vision of evidence, as well as cl ear criteria for determining the validity of evidence. In addition, teachers should be trained to mobilise systematic pedagogic know ledge and transform their practice, using available institutional support, the systematic analysis of their own experience, and the promotion of best practic

Transferring Collective Knowledge: Collective and Fragmented Teaching and Learning in the Chinese Auto Industry

Collective knowledge, consisting of tacit group-embedded knowledge, is a key element of organizational capabilities. This study undertakes a multiple-case study of the transfer of collective knowledge, guided by a set of tentative constructs and propositions derived from organizational learning theory. By focusing on the group-embeddedness dimension of collective knowledge, we direct our attention to the source and recipient communities. We identify two sets of strategic choices concerning the transfer of collective knowledge: collective vs. fragmented teaching, and collective vs. fragmented learning. The empirical context of this study is international R&D capability transfer in the Chinese auto industry. From the case evidence, we find the expected benefits of collective teaching and collective learning, and also discover additional benefits of these two strategies, including the creation of a bridge network communication infrastructure. The study disclosed other conditions underlying the choice of strategies of transferring collective knowledge, including transfer effort and the level of group-embeddedness of the knowledge to be taught or re-embedded. The paper provides a group-level perspective in understanding organizational capabilities, as well as a set of refined constructs and propositions concerning strategic choices of transferring collective knowledge. The study also provides a rich description of the best practices and lessons learned in transferring organizational capabilities.http://deepblue.lib.umich.edu/bitstream/2027.42/39804/3/wp420.pd

Pilot Evaluation of the Mexican Model of Dual TVET in the State of Mexico

Since the first public announcement of the Mexican Model of Dual TVET (MMFD) in June 2013, more than 5,000 apprentices have enrolled in the programme and around 2,000 already graduated. The Ministry of Education (SEP and CONALEP), the Chambers of Commerce (i.e. COPARMEX) and the German Cooperation Agencies (i.e. CAMEXA) have been collaborating with state authorities, families, schools and companies to turn this initial idea into a significant and sustainable initiative. Although the numbers are still small, it seemed necessary to undertake a pilot evaluation study of the implementation and impact of this program on its participants to inform those responsible for this policy. We decided to focus our study on the State of Mexico because of the higher number of apprentices in this state and because of the access that the CONALEP authorities gave us to the informants. The report that you are about to read is structured in four main sections. In the first one we reviewed the international evidence on the experiences of policy transfer of Dual TVET. Transferring international good practice sin TVET is always a complex process that requires careful attention to the experiences and lessons from those that tried to do it before. In the second section, we present the main characteristics of the Mexican Model of Dual TVET and the specificities of its implementation in the State of Mexico. In a federal country like Mexico, it is important to understand that national policies may largely vary across states in terms of design and implementation. The third section outlines the methodology of the study, which is inspired by the realist evaluation principles. Realist evaluation, not only tries to measure the impact of interventions on beneficiaries, but also to understand the causal mechanisms that explain why this policy is more effective in certain contexts and with certain beneficiary populations than in others. In the final section, the results of the interviews and the survey with 25 apprentices that completed their studies under the MMFD in the State of Mexico are presented. Obviously, the reduced sample of the study limits the representativeness of our findings but it will offer some expected and unexpected results that should not be ignored by those involved in this policy in the State of Mexico and nationally

Externalities in North-South technology transfer: the case of CNG engines in Iran

This contribution focuses on illuminating the challenges and difficulties of North-South technology transfer. The central message of this paper is that North-South technology transfer is not simply a contract between two transacting firms and does not depend only on intra-firm and inter-firm factors. The process may also be influenced by a number of external factors, beyond the control or power of project managers. However, understanding of these external factors greatly influences the success of firms' technological development. These externalities could arise from North-South contexts variances, international atmosphere and even by different levels of both sides' actors involved in the process. Using an in-depth case study analysis for collaboration between Iranian and German companies, this article develops a clearer understanding of external factors which affect the cross-border technology transfer process

The educational research-practice interface revisited

The question of how the realms of research and practice might successfully relate to one another is a persisting one, and especially so in education. The article takes a fresh look at this issue by using the terminology of collaboration scripts to reflect upon various forms of this relationship. Under this perspective, several approaches towards bridging the research/ practice gap are being described with regard to the type and closeness of interaction between the two realms. As different focuses and blind spots become discernible, the issue is raised concerning which 'script' might be appropriate depending upon the starting conditions of research interacting with practice

Grounding Language for Transfer in Deep Reinforcement Learning

In this paper, we explore the utilization of natural language to drive transfer for reinforcement learning (RL). Despite the wide-spread application of deep RL techniques, learning generalized policy representations that work across domains remains a challenging problem. We demonstrate that textual descriptions of environments provide a compact intermediate channel to facilitate effective policy transfer. Specifically, by learning to ground the meaning of text to the dynamics of the environment such as transitions and rewards, an autonomous agent can effectively bootstrap policy learning on a new domain given its description. We employ a model-based RL approach consisting of a differentiable planning module, a model-free component and a factorized state representation to effectively use entity descriptions. Our model outperforms prior work on both transfer and multi-task scenarios in a variety of different environments. For instance, we achieve up to 14% and 11.5% absolute improvement over previously existing models in terms of average and initial rewards, respectively.Comment: JAIR 201