112,287 research outputs found
Fostering the reduction of assortative mixing or homophily into the class
Human societies from the outset have been associated according to race, beliefs, religion, social level, and the like. These behaviors continue even today in the classroom at primary, middle, and superior levels. However, the growth of ICT offers educational researchers new ways to explore methods of team formation that have been proven to be efficient in the field of serious games through the use of computer networks. The selection process of team members in serious games through the use of computer networks is carried out according to their performance in the area of the game without distinction of social variables.
The use of serious games in education has been discussed in multiple research studies which state that its application in teaching and learning processes are changing the way of teaching. This article presents an exploratory analysis of the team formation process based on collaboration through the use of ICT tools of collective intelligence called TBT (The best team). The process and its ICT tool combine the paradigms of creativity in swarming, collective intelligence, serious games, and social computing in order to capture the participants’ emotions and evaluate contributions.
Based on the results, we consider that the use of new forms of teaching and learning based on the emerging paradigms is necessary. Therefore, TBT is a tool that could become an effective way to encourage the formation of work groups by evaluating objective variable of performance of its members in collaborative works.Postprint (published version
Overview: Computer vision and machine learning for microstructural characterization and analysis
The characterization and analysis of microstructure is the foundation of
microstructural science, connecting the materials structure to its composition,
process history, and properties. Microstructural quantification traditionally
involves a human deciding a priori what to measure and then devising a
purpose-built method for doing so. However, recent advances in data science,
including computer vision (CV) and machine learning (ML) offer new approaches
to extracting information from microstructural images. This overview surveys CV
approaches to numerically encode the visual information contained in a
microstructural image, which then provides input to supervised or unsupervised
ML algorithms that find associations and trends in the high-dimensional image
representation. CV/ML systems for microstructural characterization and analysis
span the taxonomy of image analysis tasks, including image classification,
semantic segmentation, object detection, and instance segmentation. These tools
enable new approaches to microstructural analysis, including the development of
new, rich visual metrics and the discovery of
processing-microstructure-property relationships.Comment: submitted to Materials and Metallurgical Transactions
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Harnessing agile concepts for the development of intelligent systems
Traditional and current approaches to intelligent systems design, have led to the creation of sophisticated and computationally-intensive packages and environments, for a wide range of applications. This paper proposes methods with which to extend the functionality of such systems, borrowing knowledge management concepts from the field of Agile Manufacturing. As such, this paper proposes that the future of intelligent systems design should be based not only upon the continuing development of artificial intelligence techniques, but also effective methods for harnessing human skills and core competencies to achieve these aims
The Profiling Potential of Computer Vision and the Challenge of Computational Empiricism
Computer vision and other biometrics data science applications have commenced
a new project of profiling people. Rather than using 'transaction generated
information', these systems measure the 'real world' and produce an assessment
of the 'world state' - in this case an assessment of some individual trait.
Instead of using proxies or scores to evaluate people, they increasingly deploy
a logic of revealing the truth about reality and the people within it. While
these profiling knowledge claims are sometimes tentative, they increasingly
suggest that only through computation can these excesses of reality be captured
and understood. This article explores the bases of those claims in the systems
of measurement, representation, and classification deployed in computer vision.
It asks if there is something new in this type of knowledge claim, sketches an
account of a new form of computational empiricism being operationalised, and
questions what kind of human subject is being constructed by these
technological systems and practices. Finally, the article explores legal
mechanisms for contesting the emergence of computational empiricism as the
dominant knowledge platform for understanding the world and the people within
it
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