Duplicate Frame Detection in Forged Videos Using Sequence Matching

Keep it simple - A case study of model development in the context of the Dynamic Stocks and Flows (DSF) taskThis paper describes the creation of a cognitive model submitted to the ‘Dynamic Stocks and Flows’ (DSF) modeling challenge. This challenge aims at comparing computational cognitive models for human behavior during an open ended control task. Participants in the modeling competition were provided with a simulation environment and training data for benchmarking their models while the actual specification of the competition task was withheld. To meet this challenge, the cognitive model described here was designed and optimized for generalizability. Only two simple assumptions about human problem solving were used to explain the empirical findings of the training data. In-depth analysis of the data set prior to the development of the model led to the dismissal of correlations or other parametric statistics as goodness-of-fit indicators. A new statistical measurement based on rank orders and sequence matching techniques is being proposed instead. This measurement, when being applied to the human sample, also identifies clusters of subjects that use different strategies for the task. The acceptability of the fits achieved by the model is verified using permutation tests.

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Curriculum Assessment of Higher Educational Institution Using Trace-segmented Clustering

Jurnal Teknik Industri ◽

10.9744/jti.20.1.33-48 ◽

2018 ◽

Vol 20 (1) ◽

pp. 33-48

Author(s):

Satrio Adi Priyambada ◽

Mahendrawathi ER ◽

Bernardo Nugroho Yahya

Keyword(s):

Educational Institution ◽

Research Area ◽

Learning Performance ◽

Learning Behavior ◽

Sequence Matching ◽

Curriculum Assessment ◽

Learning Path ◽

And Performance ◽

Curriculum Guideline ◽

The Impact

Curriculum mining is research area that assess students’ learning behavior and compare it with the curriculum guideline. Previous work developed sequence matching alignment approach to check the conformance between students’ learning behavior and curriculum guideline. Considering only the sequence matching alignment is insufficient to understand the patterns of group of students. Another work proposed an approach by aggregating the students’ profile to represent students’ learning behavior and investigate the impact of the learning behavior to their learning performance. However, the aggregate profile approach considers the entire period of study rather than segmented period. This study proposes a methodology to assess students’ learning path with segmented period i.e. the semester of the related curriculum. The segmented-period profile generated would be the input for sequence matching alignment approach to assess the conformity of students’ behavior with the prior curriculum guideline. Real curriculum data has been used to test the effectivity of the methodology. The results show that the students can be grouped into various cluster per semesters that have different characteristic with respect to their learning behavior and performance. The results can be analyzed further to improve the curriculum guideline.

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HCLBLAST for Genome Sequence Matching

International Journal of Computer Applications ◽

10.5120/ijca2017913777 ◽

2017 ◽

Vol 163 (11) ◽

pp. 31-34

Author(s):

Monika Yadav ◽

Sonal Chaudhary

Keyword(s):

Genome Sequence ◽

Sequence Matching

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Distribution bias of the sequence matching between exons and introns in exon joint and EJC binding region in C. elegans

Journal of Theoretical Biology ◽

10.1016/j.jtbi.2014.09.009 ◽

2015 ◽

Vol 364 ◽

pp. 295-304 ◽

Cited By ~ 4

Author(s):

Qiang Zhang ◽

Hong Li ◽

Xiaoqing Zhao ◽

Yan Zheng ◽

Deliang Zhou

Keyword(s):

Sequence Matching ◽

Binding Region ◽

C Elegans

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A Low-Dimensional Radial Silhouette-Based Feature for Fast Human Action Recognition Fusing Multiple Views

International Scholarly Research Notices ◽

10.1155/2014/547069 ◽

2014 ◽

Vol 2014 ◽

pp. 1-11 ◽

Cited By ~ 8

Author(s):

Alexandros Andre Chaaraoui ◽

Francisco Flórez-Revuelta

Keyword(s):

Real Time ◽

Action Recognition ◽

Assisted Living ◽

Learning Algorithm ◽

Ambient Assisted Living ◽

Human Action Recognition ◽

Human Action ◽

Sequence Matching ◽

Low Dimensional ◽

Video Frequency

This paper presents a novel silhouette-based feature for vision-based human action recognition, which relies on the contour of the silhouette and a radial scheme. Its low-dimensionality and ease of extraction result in an outstanding proficiency for real-time scenarios. This feature is used in a learning algorithm that by means of model fusion of multiple camera streams builds a bag of key poses, which serves as a dictionary of known poses and allows converting the training sequences into sequences of key poses. These are used in order to perform action recognition by means of a sequence matching algorithm. Experimentation on three different datasets returns high and stable recognition rates. To the best of our knowledge, this paper presents the highest results so far on the MuHAVi-MAS dataset. Real-time suitability is given, since the method easily performs above video frequency. Therefore, the related requirements that applications as ambient-assisted living services impose are successfully fulfilled.

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