Pitfalls in Applying Model Learning to Industrial Legacy Software

2018 ◽  
pp. 121-138 ◽  
Author(s):  
Omar al Duhaiby ◽  
Arjan Mooij ◽  
Hans van Wezep ◽  
Jan Friso Groote
Keyword(s):  
Model Learning ◽  
Legacy Software

An Expert Data-Driven Air Combat Maneuver Model Learning Approach

2021 ◽  
Author(s):  
Su-Jeong Park ◽  
Soon-Seo Park ◽  
Han-Lim Choi ◽  
Kyeong-Soo An ◽  
Young-Gon Kim
Keyword(s):  
Data Driven ◽  
Learning Approach ◽  
Model Learning ◽  
Air Combat ◽  
Expert Data

PERBAIKAN KUALITAS PEMBELAJARAN DAN HASIL BELAJAR SISWA DENGAN MODEL LEARNING CYCLE COOPERATIVE 5 FASE DALAM PENGAJARAN KIMIA

2015 ◽  
Vol 7 (1) ◽  
pp. 29-38
Author(s):  
Esti Munafiah ◽  
Agus Basir Ali Akbar S
Keyword(s):  
Action Research ◽  
Learning Process ◽  
Mastery Learning ◽  
Learning Cycle ◽  
Model Learning ◽  
Classroom Action Research ◽  
Academic Year

The objective of this study is to see learning process using LCC model for chemistry course.  The study used classroom action research with three cycles each of which implements planning, acting, observing and reflection.  Subject of the study was 40 students of grade 8E of MTsN Blitar in the academic year 2009/2010. The findings of the study are as follows:  (1) Cycle I:  students participation 62.5%, mean score of worksheet 60, mean score of quiz 41,7, and mastery learning 3 students; (2) Cycle II: students participation 86.6%, mean score of worksheet 81, mean score of quiz 72.38, and mastery learning 26 students; (3) Cycle III:  students participation 100%, mean score of worksheet 89, mean score of quiz 72.44, and mastery learning 39 students.

Web Service Candidate Identification Using the Firefly Algorithm

2018 ◽  
Vol 15 (4) ◽  
pp. 45-60
Author(s):  
Negar Abbasi ◽  
Ali Moeini ◽  
Taghi Javdani Gandomani
Keyword(s):  
Web Service ◽  
Firefly Algorithm ◽  
Service Oriented Architecture ◽  
High Reliability ◽  
Legacy Software ◽  
Service Identification ◽  
Service Oriented ◽  
Automatic Methods ◽  
Reservation System ◽  
Candidate Identification

Identification of web service candidates in legacy software is a crucial process in the reengineering of legacy systems to service oriented architecture. Researchers have proposed various automatic and semi-automatic methods for this purpose, some of which have proved to be quite efficient, but there are still certain gaps which need to be addressed. This article discovers the strengths and weaknesses of previous methods and develops a method with improved service candidate identification performance. In this article, service identification is considered as a search and optimization problem and a firefly algorithm is developed accordingly to give high-quality solutions in reasonably short times. A filtering method is also developed to remove excess modules (false positives) from the algorithm outputs. A case study on a legacy flight reservation system demonstrates the high reliability of the outputs given by the proposed method.

scholarly journals NeuSE: A Neural Snapshot Ensemble Method for Collaborative Filtering

2021 ◽  
Vol 15 (6) ◽  
pp. 1-20
Author(s):  
Dongsheng Li ◽  
Haodong Liu ◽  
Chao Chen ◽  
Yingying Zhao ◽  
Stephen M. Chu ◽  
...  
Keyword(s):  
Collaborative Filtering ◽  
Optimization Problems ◽  
Empirical Studies ◽  
Large Datasets ◽  
Model Learning ◽  
Global Models ◽  
Convex Optimization Problems ◽  
Memory Network ◽  
Real World Datasets ◽  
Performance Tradeoff

In collaborative filtering (CF) algorithms, the optimal models are usually learned by globally minimizing the empirical risks averaged over all the observed data. However, the global models are often obtained via a performance tradeoff among users/items, i.e., not all users/items are perfectly fitted by the global models due to the hard non-convex optimization problems in CF algorithms. Ensemble learning can address this issue by learning multiple diverse models but usually suffer from efficiency issue on large datasets or complex algorithms. In this article, we keep the intermediate models obtained during global model learning as the snapshot models, and then adaptively combine the snapshot models for individual user-item pairs using a memory network-based method. Empirical studies on three real-world datasets show that the proposed method can extensively and significantly improve the accuracy (up to 15.9% relatively) when applied to a variety of existing collaborative filtering methods.

scholarly journals Offensive keyword extraction based on the attention mechanism of BERT and the eigenvector centrality using a graph representation

2021 ◽  
Author(s):  
Gretel Liz De la Peña Sarracén ◽  
Paolo Rosso
Keyword(s):  
Language Model ◽  
Hybrid Approach ◽  
Attention Mechanism ◽  
Graph Representation ◽  
Keyword Extraction ◽  
Eigenvector Centrality ◽  
Model Learning ◽  
Harmful Content ◽  
The Impact ◽  
Offensive Language

AbstractThe proliferation of harmful content on social media affects a large part of the user community. Therefore, several approaches have emerged to control this phenomenon automatically. However, this is still a quite challenging task. In this paper, we explore the offensive language as a particular case of harmful content and focus our study in the analysis of keywords in available datasets composed of offensive tweets. Thus, we aim to identify relevant words in those datasets and analyze how they can affect model learning. For keyword extraction, we propose an unsupervised hybrid approach which combines the multi-head self-attention of BERT and a reasoning on a word graph. The attention mechanism allows to capture relationships among words in a context, while a language model is learned. Then, the relationships are used to generate a graph from what we identify the most relevant words by using the eigenvector centrality. Experiments were performed by means of two mechanisms. On the one hand, we used an information retrieval system to evaluate the impact of the keywords in recovering offensive tweets from a dataset. On the other hand, we evaluated a keyword-based model for offensive language detection. Results highlight some points to consider when training models with available datasets.

Simultaneous State Estimation and Tire Model Learning for Autonomous Vehicle Applications

2021 ◽  
pp. 1-1
Author(s):  
Woongsun Jeon ◽  
Ankush Chakrabarty ◽  
Ali Zemouche ◽  
Rajesh Rajamani
Keyword(s):  
State Estimation ◽  
Autonomous Vehicle ◽  
Tire Model ◽  
Model Learning
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