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Models of Scholarly Communication and Citation Analysis
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| Fredrik Astrom, Agnes Sandor |
| ISSI 2009, 12th International Conference on Scientometrics and Informetrics, Rio de Janeiro, Brazil, July 14-17, 2009. The article is available on this internet website : ISSI Website |
Informetric/bibliometric analyses have to a large extent been relying on an assumption that research is essentially cumulative in its nature, which is not the least visible in the rational for using citation analyses to assess quality of research. However, when reviewing both the theoretical literature on how research is organized and studies analyzing the structures of research fields through informetric mapping methods, it becomes clear that cumulative organization is just one category of several ways of organizing research and scholarly communication, Consequently, the way the role of citations is interpreted in research assessment has to be revised. Based on the review of previous research, this paper suggests a model for categorizing different modes of scholarly communication. We test this model through three different kinds of semantic labelling analyses on abstracts and research papers from the fields of biomedicine, computer science and educational research. The model proposed suggests three main categories of scholarly communication: cumulative, negotiating and distinctive; and when matching the labels identified in the semantic analysis to the three categories, we find evidence of the three different ways of communicating research that supports the model.
To make robots autonomous in real-world everyday spaces, they should be able to learn from their interactions within these spaces, how to best execute tasks specified by non-expert users in a safe and reliable way. To do so requires sequential decision-making skills that combine machine learning, adaptive planning and control in uncertain environments as well as solving hard combinatorial optimisation problems. Our research combines expertise in reinforcement learning, computer vision, robotic control, sim2real transfer, large multimodal foundation models and neural combinatorial optimisation to build AI-based architectures and algorithms to improve robot autonomy and robustness when completing everyday complex tasks in constantly changing environments.
VISION
The research we conduct on expressive visual representations is applicable to visual search, object detection, image classification and the automatic extraction of 3D human poses and shapes that can be used for human behavior understanding and prediction, human-robot interaction or even avatar animation. We also extract 3D information from images that can be used for intelligent robot navigation, augmented reality and the 3D reconstruction of objects, buildings or even entire cities.
Our work covers the spectrum from unsupervised to supervised approaches, and from very deep architectures to very compact ones. We’re excited about the promise of big data to bring big performance gains to our algorithms but also passionate about the challenge of working in data-scarce and low-power scenarios.
Furthermore, we believe that a modern computer vision system needs to be able to continuously adapt itself to its environment and to improve itself via lifelong learning. Our driving goal is to use our research to deliver embodied intelligence to our users in robotics, autonomous driving, via phone cameras and any other visual means to reach people wherever they may be.
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