Special Issue on Large Scale and Nonlinear Similarity Learning for intelleigent Video Analysis

AbstractContributors to this Special Issue of the Cognitive Behaviour Therapist have considered the kind of infrastructure that should be in place to best support and guide CBT supervisors, providing practical advice and extensive procedural guidance. Here we briefly summarize and discuss in turn the 10 papers within this Special Issue, including suggestions for further enhancements. The first paper, by Milne and Reiser, conceptualized this infrastructure in terms of an ‘SOS’ (supporting our supervisors) framework, from identifying supervision competencies, to training, evaluation and feedback strategies. The next nine papers illustrate this framework with specific technical innovations, educational enhancements and procedural issues, or through comprehensive quality improvement systems, all designed to support supervisors. These papers suggest an assortment of workable infrastructure developments: two large-scale and comprehensive initiatives, some promising proposals and technologies, and a series of local, exploratory work. Collectively, they provide us with models for further developing evidence-based cognitive-behavioural supervision, and offer practical suggestions for giving supervisors the tools and support to maximize their supervisees’ learning, and to improve the associated client outcomes. Much research and development work remains to be done, and successful implementation will require institutional and political support, as well as cross-cultural adaptations. We conclude with an optimistic assessment of progress toward addressing some of the infrastructure improvements required to adequately support supervisors.

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Application Research of Key Frames Extraction Technology Combined with Optimized Faster R-CNN Algorithm in Traffic Video Analysis

Complexity ◽

10.1155/2021/6620425 ◽

2021 ◽

Vol 2021 ◽

pp. 1-11

Author(s):

Zhi-guang Jiang ◽

Xiao-tian Shi

Keyword(s):

Video Analysis ◽

Management System ◽

Large Scale ◽

Data Communication ◽

Transportation System ◽

Extraction Technology ◽

Transportation Management ◽

Key Frame Extraction ◽

Original Algorithm ◽

Key Frame

The intelligent transportation system under the big data environment is the development direction of the future transportation system. It effectively integrates advanced information technology, data communication transmission technology, electronic sensing technology, control technology, and computer technology and applies them to the entire ground transportation management system to establish a real-time, accurate, and efficient comprehensive transportation management system that works on a large scale and in all directions. Intelligent video analysis is an important part of smart transportation. In order to improve the accuracy and time efficiency of video retrieval schemes and recognition schemes, this article firstly proposes a segmentation and key frame extraction method for video behavior recognition, using a multi-time scale dual-stream network to extract video features, improving the efficiency and efficiency of video behavior detection. On this basis, an improved algorithm for vehicle detection based on Faster R-CNN is proposed, and the Faster R-CNN network feature extraction layer is improved by using the principle of residual network, and a hole convolution is added to the network to filter out the redundant features of high-resolution video images to improve the problem of vehicle missed detection in the original algorithm. The experimental results show that the key frame extraction technology combined with the optimized Faster R-CNN algorithm model greatly improves the accuracy of detection and reduces the leakage. The detection rate is satisfactory.

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Introduction to special issue: Learning assessments for sustainability? Exploring the interaction between two global movements

Education Policy Analysis Archives ◽

10.14507/epaa.29.7171 ◽

2021 ◽

Vol 29 ◽

pp. 121

Author(s):

Oren Pizmony-Levy ◽

Dafna Gan

Keyword(s):

Large Scale ◽

Secondary Analysis ◽

Lessons Learned ◽

Negative Consequences ◽

Special Issue ◽

Learning Assessments ◽

Introductory Article ◽

Meaningful Engagement ◽

Global Movements ◽

The 1960S

The aim of this special issue, “Learning Assessments for Sustainability?”, is to examine the interaction between the environmental and sustainability education (ESE) movement and the international large-scale assessments (ILSAs) movement. Both global educational movements emerged in the 1960s and their simultaneous work have affected each other since then. While the articles in this special issue highlight the potential benefits of ILSAs as a source of data for secondary analysis, they also demonstrate the limitations of ILSAs and their negative consequences to ESE. As such, we call for more research on the interaction between ESE and ILSAs and for a serious consideration of how test-based accountability practices might work against meaningful engagement with ESE. This introductory article includes three sections. The first section provides context about the movements. The second section presents an overview of the articles and alternative ways for reading them. The third section discusses lessons learned from the collection of articles. We conclude with a call for further research and reflection.

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