Model Learning as a Satisfiability Modulo Theories Problem

2018 ◽  
pp. 182-194 ◽  
Author(s):  
Rick Smetsers ◽  
Paul Fiterău-Broştean ◽  
Frits Vaandrager
Keyword(s):  
Satisfiability Modulo Theories ◽  
Model Learning

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

Engineering Theories with Z3

10.29007/x7b4 ◽  
2018 ◽  
Author(s):  
Nikolaj Bjorner
Keyword(s):  
Satisfiability Modulo Theories ◽  
Smt Solver ◽  
Testing Tools ◽  
Software And Hardware ◽  
Design And Testing

Modern Satisfiability Modulo Theories (SMT)solvers are fundamental to many programanalysis, verification, design and testing tools. They are a goodfit for the domain of software and hardware engineering becausethey support many domains that are commonly used by the tools.The meaning of domains are captured by theories that can beaxiomatized or supported by efficient <i>theory solvers</i>.Nevertheless, not all domains are handled by all solvers andmany domains and theories will never be native to any solver.We here explore different theories that extend MicrosoftResearch's SMT solver Z3's basicsupport. Some can be directly encoded or axiomatized,others make use of user theory plug-ins.Plug-ins are a powerful way for tools to supply their custom domains.

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.

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

Knowledge-aware Multi-modal Adaptive Graph Convolutional Networks for Fake News Detection

2021 ◽  
Vol 17 (3) ◽  
pp. 1-23
Author(s):  
Shengsheng Qian ◽  
Jun Hu ◽  
Quan Fang ◽  
Changsheng Xu
Keyword(s):  
Social Media ◽  
Visual Information ◽  
Representation Learning ◽  
Fake News ◽  
Unified Framework ◽  
Model Learning ◽  
Convolutional Network ◽  
Textual Information ◽  
Convolutional Networks ◽  
Real World Datasets

In this article, we focus on fake news detection task and aim to automatically identify the fake news from vast amount of social media posts. To date, many approaches have been proposed to detect fake news, which includes traditional learning methods and deep learning-based models. However, there are three existing challenges: (i) How to represent social media posts effectively, since the post content is various and highly complicated; (ii) how to propose a data-driven method to increase the flexibility of the model to deal with the samples in different contexts and news backgrounds; and (iii) how to fully utilize the additional auxiliary information (the background knowledge and multi-modal information) of posts for better representation learning. To tackle the above challenges, we propose a novel Knowledge-aware Multi-modal Adaptive Graph Convolutional Networks (KMAGCN) to capture the semantic representations by jointly modeling the textual information, knowledge concepts, and visual information into a unified framework for fake news detection. We model posts as graphs and use a knowledge-aware multi-modal adaptive graph learning principal for the effective feature learning. Compared with existing methods, the proposed KMAGCN addresses challenges from three aspects: (1) It models posts as graphs to capture the non-consecutive and long-range semantic relations; (2) it proposes a novel adaptive graph convolutional network to handle the variability of graph data; and (3) it leverages textual information, knowledge concepts and visual information jointly for model learning. We have conducted extensive experiments on three public real-world datasets and superior results demonstrate the effectiveness of KMAGCN compared with other state-of-the-art algorithms.

Sign in / Sign up

Export Citation Format

Share Document