scholarly journals Inferring Concept Prerequisite Relations from Online Educational Resources

2019 ◽  
Vol 33 ◽  
pp. 9589-9594 ◽  
Author(s):  
Sudeshna Roy ◽  
Meghana Madhyastha ◽  
Sheril Lawrence ◽  
Vaibhav Rajan
Keyword(s):  
Network Architecture ◽  
Large Scale ◽  
Latent Dirichlet Allocation ◽  
Training Data ◽  
Allocation Model ◽  
Latent Dirichlet Allocation Model ◽  
Technology Applications ◽  
Benchmark Datasets ◽  
Latent Representations ◽  
Online Educational Resources

The Internet has rich and rapidly increasing sources of high quality educational content. Inferring prerequisite relations between educational concepts is required for modern large-scale online educational technology applications such as personalized recommendations and automatic curriculum creation. We present PREREQ, a new supervised learning method for inferring concept prerequisite relations. PREREQ is designed using latent representations of concepts obtained from the Pairwise Latent Dirichlet Allocation model, and a neural network based on the Siamese network architecture. PREREQ can learn unknown concept prerequisites from course prerequisites and labeled concept prerequisite data. It outperforms state-of-the-art approaches on benchmark datasets and can effectively learn from very less training data. PREREQ can also use unlabeled video playlists, a steadily growing source of training data, to learn concept prerequisites, thus obviating the need for manual annotation of course prerequisites.

scholarly journals Crowd Event Perception Based on Spatiotemporal Weber Field

2014 ◽  
Vol 2014 ◽  
pp. 1-12 ◽  
Author(s):  
Zhou Su ◽  
Hua Wei ◽  
Sha Wei
Keyword(s):  
Large Scale ◽  
Latent Dirichlet Allocation ◽  
Interaction Force ◽  
Motion Modeling ◽  
Allocation Model ◽  
Motion Patterns ◽  
Crowd Management ◽  
Latent Dirichlet Allocation Model ◽  
Spatiotemporal Signal ◽  
Crowd Motion

Over the past decade, a wide attention has been paid to the crowd control and management in intelligent video surveillance area. Among the tasks of automatic video-based crowd management, crowd motion modeling is recognized as one of the most critical components, since it lays a crucial foundation for numerous subsequent analyses. However, it still encounters many unsolved challenges due to occlusions among pedestrians, complicated motion patterns in crowded scenarios, and so forth. Addressing these issues, we propose a novel spatiotemporal Weber field, which integrates both appearance characteristics and stimulus of crowd motion patterns, to recognize the large-scale crowd event. On the one hand, crowd motion is recognized as variations of spatiotemporal signal, and we then measure the variation based on Weber law. The result is referred to as spatiotemporal Weber variation feature. On the other hand, motivated by the achievements in crowd dynamics that crowd motion has a close relationship with interaction force, we propose a spatiotemporal Weber force feature to exploit the stimulus of crowd behaviors. Finally, we utilize the latent Dirichlet allocation model to establish the relationship between crowd events and crowd motion patterns. Experiments on PETS2009 and UMN databases demonstrate that our proposed method outperforms the previous methods for the large-scale crowd behavior perception.

scholarly journals Speeding up calibration of latent Dirichlet allocation model to improve topic analysis in software engineering

2021 ◽  
Author(s):  
Jorge Arturo Lopez
Keyword(s):  
Software Engineering ◽  
Simple Formula ◽  
Latent Dirichlet Allocation ◽  
Allocation Model ◽  
Topic Analysis ◽  
Latent Dirichlet Allocation Model ◽  
Related Text ◽  
The Empirical Analysis ◽  
Large Corpus ◽  
Dirichlet Allocation

Extraction of topics from large text corpuses helps improve Software Engineering (SE) processes. Latent Dirichlet Allocation (LDA) represents one of the algorithmic tools to understand, search, exploit, and summarize a large corpus of data (documents), and it is often used to perform such analysis. However, calibration of the models is computationally expensive, especially if iterating over a large number of topics. Our goal is to create a simple formula allowing analysts to estimate the number of topics, so that the top X topics include the desired proportion of documents under study. We derived the formula from the empirical analysis of three SE-related text corpuses. We believe that practitioners can use our formula to expedite LDA analysis. The formula is also of interest to theoreticians, as it suggests that different SE text corpuses have similar underlying properties.

Engineering for Global Development: Characterizing the Discipline Through a Systematic Literature Review

2020 ◽  
Author(s):  
Grace Burleson ◽  
Jesse Austin-Breneman
Keyword(s):  
Literature Review ◽  
Systematic Literature Review ◽  
Mechanical Engineering ◽  
Latent Dirichlet Allocation ◽  
Descriptive Analysis ◽  
Secondary Data ◽  
Research Area ◽  
Global Development ◽  
Allocation Model ◽  
Latent Dirichlet Allocation Model

Abstract Over the past 50 years, researchers have repeatedly proposed the establishment of a new interdisciplinary engineering field in Engineering for Global Development (EGD), whose analytical tools and design processes result in positive social impacts and poverty alleviation in a global development context. Within each discipline and research area, a growing body of work has sought to systematically create scientific knowledge in this area. However, a recent network analysis of Human-Centered Design plus Development research indicates that sub-communities are not collaborating at a high level and therefore the overall research agenda may lack cohesion. This paper presents a descriptive analysis of EGD research within mechanical engineering along four dimensions through a systematic literature review and secondary data analysis. Results from the review and a Latent Dirichlet Allocation model indicate EGD work in mechanical engineering draws upon research methodologies from a number of other fields and has low levels of consensus on technical terminology. These results suggest consensus in the broader interdisciplinary EGD field should be examined.

scholarly journals An Influence Prediction Model for Microblog Entries on Public Health Emergencies

2019 ◽  
Vol 3 (2) ◽  
pp. 102-115 ◽  
Author(s):  
Lu An ◽  
Xingyue Yi ◽  
Yuxin Han ◽  
Gang Li
Keyword(s):  
Public Health ◽  
Prediction Model ◽  
Latent Dirichlet Allocation ◽  
Prediction Models ◽  
Data Sets ◽  
Allocation Model ◽  
Latent Dirichlet Allocation Model ◽  
Public Health Emergencies ◽  
Proposed Model ◽  
The Individual

Abstract This study aims at constructing a microblog influence prediction model and revealing how the user, time, and content features of microblog entries about public health emergencies affect the influence of microblog entries. Microblog entries about the Ebola outbreak are selected as data sets. The BM25 latent Dirichlet allocation model (LDA-BM25) is used to extract topics from the microblog entries. A microblog influence prediction model is proposed by using the random forest method. Results reveal that the proposed model can predict the influence of microblog entries about public health emergencies with a precision rate reaching 88.8%. The individual features that play a role in the influence of microblog entries, as well as their influence tendencies are also analyzed. The proposed microblog influence prediction model consists of user, time, and content features. It makes up the deficiency that content features are often ignored by other microblog influence prediction models. The roles of the three features in the influence of microblog entries are also discussed.

scholarly journals Attention-Aware Adversarial Network for Person Re-Identification

2019 ◽  
Vol 9 (8) ◽  
pp. 1550 ◽  
Author(s):  
Aihong Shen ◽  
Huasheng Wang ◽  
Junjie Wang ◽  
Hongchen Tan ◽  
Xiuping Liu ◽  
...  
Keyword(s):  
High Performance ◽  
Large Scale ◽  
Data Augmentation ◽  
Fundamental Problem ◽  
Training Data ◽  
Specific Data ◽  
Training Strategy ◽  
Adversarial Network ◽  
Benchmark Datasets ◽  
Adversarial Training

Person re-identification (re-ID) is a fundamental problem in the field of computer vision. The performance of deep learning-based person re-ID models suffers from a lack of training data. In this work, we introduce a novel image-specific data augmentation method on the feature map level to enforce feature diversity in the network. Furthermore, an attention assignment mechanism is proposed to enforce that the person re-ID classifier focuses on nearly all important regions of the input person image. To achieve this, a three-stage framework is proposed. First, a baseline classification network is trained for person re-ID. Second, an attention assignment network is proposed based on the baseline network, in which the attention module learns to suppress the response of the current detected regions and re-assign attentions to other important locations. By this means, multiple important regions for classification are highlighted by the attention map. Finally, the attention map is integrated in the attention-aware adversarial network (AAA-Net), which generates high-performance classification results with an adversarial training strategy. We evaluate the proposed method on two large-scale benchmark datasets, including Market1501 and DukeMTMC-reID. Experimental results show that our algorithm performs favorably against the state-of-the-art methods.

Research Topic Analysis in Engineering Management Using a Latent Dirichlet Allocation Model

2018 ◽  
Vol 03 (04) ◽  
pp. 1850016 ◽  
Author(s):  
Jin Ho Kim ◽  
Weiru Chen
Keyword(s):  
Latent Dirichlet Allocation ◽  
Probabilistic Models ◽  
Engineering Management ◽  
Research Topic ◽  
Analysis Model ◽  
Research Topics ◽  
Allocation Model ◽  
Topic Analysis ◽  
Latent Dirichlet Allocation Model ◽  
Research Areas

Traditional journal analyses of topic trends in IS journals have manually coded target articles from chosen time periods. However, some research efforts have been made to apply automatic bibliometric approaches, such as cluster analysis and probabilistic models, to find topics in academic articles in other research areas. The purpose of this study is thus to investigate research topic trends in Engineering Management from 1998 through 2017 using an LDA analysis model. By investigating topics in EM journals, we provide partial but meaningful trends in EM research topics. The trend analysis shows that there are hot topics with increasing numbers of articles, steady topics that remain constant, and cold topics with decreasing numbers of articles.

Sign in / Sign up

Export Citation Format

Share Document