Combining low-, high-level and empirical domain knowledge for automated segmentation of ultrasonic breast lesions

2003 ◽  
Vol 22 (2) ◽  
pp. 155-169 ◽  
Author(s):  
A. Madabhushi ◽  
D.N. Metaxas
Keyword(s):  
Domain Knowledge ◽  
Breast Lesions ◽  
Automated Segmentation ◽  
High Level

Optical Music Analysis for Printed Music Score and Handwritten Music Manuscript

2011 ◽  
pp. 108-127 ◽  
Author(s):  
Kia Ng
Keyword(s):  
Domain Knowledge ◽  
Imaging System ◽  
Data Representation ◽  
Divide And Conquer ◽  
Music Score ◽  
Segmentation Approach ◽  
Printed Music ◽  
High Level ◽  
Machine Readable ◽  
Readable Format

This chapter describes an optical document imaging system to transform paper-based music scores and manuscripts into machine-readable format and a restoration system to touch-up small imperfections (for example broken stave lines and stems), to restore deteriorated master copy for reprinting. The chapter presents a brief background of this field, discusses the main obstacles, and presents the processes involved for printed music scores processing; using a divide-and-conquer approach to sub-segment compound musical symbols (e.g., chords) and inter-connected groups (e.g., beamed quavers) into lower-level graphical primitives (e.g., lines and ellipses) before recognition and reconstruction. This is followed by discussions on the developments of a handwritten manuscripts prototype with a segmentation approach to separate handwritten musical primitives. Issues and approaches for recognition, reconstruction and revalidation using basic music syntax and high-level domain knowledge, and data representation are also presented.

scholarly journals Helping Novices Avoid the Hazards of Data: Leveraging Ontologies to Improve Model Generalization Automatically with Online Data Sources

AI Magazine ◽  
2016 ◽  
Vol 37 (2) ◽  
pp. 19-32 ◽  
Author(s):  
Sasin Janpuangtong ◽  
Dylan A. Shell
Keyword(s):  
Domain Knowledge ◽  
Large Scale ◽  
Model Building ◽  
Data Extraction ◽  
Data Sources ◽  
Online Data ◽  
Model Generalization ◽  
Improve Model ◽  
Building Process ◽  
High Level

The infrastructure and tools necessary for large-scale data analytics, formerly the exclusive purview of experts, are increasingly available. Whereas a knowledgeable data-miner or domain expert can rightly be expected to exercise caution when required (for example, around fallacious conclusions supposedly supported by the data), the nonexpert may benefit from some judicious assistance. This article describes an end-to-end learning framework that allows a novice to create models from data easily by helping structure the model building process and capturing extended aspects of domain knowledge. By treating the whole modeling process interactively and exploiting high-level knowledge in the form of an ontology, the framework is able to aid the user in a number of ways, including in helping to avoid pitfalls such as data dredging. Prudence must be exercised to avoid these hazards as certain conclusions may only be supported if, for example, there is extra knowledge which gives reason to trust a narrower set of hypotheses. This article adopts the solution of using higher-level knowledge to allow this sort of domain knowledge to be used automatically, selecting relevant input attributes, and thence constraining the hypothesis space. We describe how the framework automatically exploits structured knowledge in an ontology to identify relevant concepts, and how a data extraction component can make use of online data sources to find measurements of those concepts so that their relevance can be evaluated. To validate our approach, models of four different problem domains were built using our implementation of the framework. Prediction error on unseen examples of these models show that our framework, making use of the ontology, helps to improve model generalization.

scholarly journals Creating Motivational Language Learning Experiences during the “Critical Period”

2019 ◽  
Vol 11 (3) ◽  
pp. 26
Author(s):  
Junqing Jia
Keyword(s):  
Foreign Language ◽  
Language Learning ◽  
Language Proficiency ◽  
Critical Period ◽  
Domain Knowledge ◽  
Advanced Level ◽  
Domain Specific ◽  
Language Learning Motivation ◽  
Domain Expertise ◽  
High Level

Few studies have touched upon language learning motivation of advanced-level learners of Chinese, even fewer have proposed a pedagogical framework to understand and create motivational pathways. This paper aims to fill the gap by addressing a critical period of foreign language training where students are transforming from learning the foreign language to learning domain knowledge in the foreign language. Having drawn upon Confucian concepts and contextualized curricular examples, this paper proposes a framework suggesting that learners at this stage experience a less discussed psychological complexity due to their high level of language proficiency and lack of multilingual domain capacities. They are also gradually transforming into autonomous language users who expand their social milieu through demonstrating domain expertise. As such, the pedagogical implications place an emphasis on helping advanced-level Chinese learners to establish domain-specific vision and linguistic capability so that they can perform in multicultural contexts. In particular, motivational pathways during this stage should be constructed to encourage learners to constantly reflect on their recent past self and establish visions of the future one.

scholarly journals RTPO: A Domain Knowledge Base for Robot Task Planning

Electronics ◽  
2019 ◽  
Vol 8 (10) ◽  
pp. 1105 ◽  
Author(s):  
Sun ◽  
Zhang ◽  
Chen
Keyword(s):  
Knowledge Base ◽  
Domain Knowledge ◽  
Task Planning ◽  
Specific Domain ◽  
Goal State ◽  
Ontology Language ◽  
Planning Algorithm ◽  
Domain Knowledge Base ◽  
Robot Task ◽  
High Level

Knowledge can enhance the intelligence of robots’ high-level decision-making. However, there is no specific domain knowledge base for robot task planning in this field. Aiming to represent the knowledge in robot task planning, the Robot Task Planning Ontology (RTPO) is first designed and implemented in this work, so that robots can understand and know how to carry out task planning to reach the goal state. In this paper, the RTPO is divided into three parts: task ontology, environment ontology, and robot ontology, followed by a detailed description of these three types of knowledge, respectively. The OWL (Web Ontology Language) is adopted to represent the knowledge in robot task planning. Then, the paper proposes a method to evaluate the scalability and responsiveness of RTPO. Finally, the corresponding task planning algorithm is designed based on RTPO, and then the paper conducts experiments on the basis of the real robot TurtleBot3 to verify the usability of RTPO. The experimental results demonstrate that RTPO has good performance in scalability and responsiveness, and the robot can achieve given high-level tasks based on RTPO.

scholarly journals Deep Learning to Detect Skin Cancer using Google Colab

2019 ◽  
Vol 8 (6) ◽  
pp. 2176-2183 ◽  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Deep Learning ◽  
Skin Cancer ◽  
Domain Knowledge ◽  
Error Rates ◽  
Data Set ◽  
Training Models ◽  
Learning Machine ◽  
High Level

Different mathematical models, Artificial Intelligence approach and Past recorded data set is combined to formulate Machine Learning. Machine Learning uses different learning algorithms for different types of data and has been classified into three types. The advantage of this learning is that it uses Artificial Neural Network and based on the error rates, it adjusts the weights to improve itself in further epochs. But, Machine Learning works well only when the features are defined accurately. Deciding which feature to select needs good domain knowledge which makes Machine Learning developer dependable. The lack of domain knowledge affects the performance. This dependency inspired the invention of Deep Learning. Deep Learning can detect features through self-training models and is able to give better results compared to using Artificial Intelligence or Machine Learning. It uses different functions like ReLU, Gradient Descend and Optimizers, which makes it the best thing available so far. To efficiently apply such optimizers, one should have the knowledge of mathematical computations and convolutions running behind the layers. It also uses different pooling layers to get the features. But these Modern Approaches need high level of computation which requires CPU and GPUs. In case, if, such high computational power, if hardware is not available then one can use Google Colaboratory framework. The Deep Learning Approach is proven to improve the skin cancer detection as demonstrated in this paper. The paper also aims to provide the circumstantial knowledge to the reader of various practices mentioned above.

Instant Basketball Defensive Trajectory Generation

2022 ◽  
Vol 13 (1) ◽  
pp. 1-20
Author(s):  
Wen-Cheng Chen ◽  
Wan-Lun Tsai ◽  
Huan-Hua Chang ◽  
Min-Chun Hu ◽  
Wei-Ta Chu
Keyword(s):  
Domain Knowledge ◽  
State Of The Art ◽  
Trajectory Generation ◽  
Empty Space ◽  
Objective Evaluation ◽  
Training System ◽  
Loss Functions ◽  
Art Works ◽  
High Level ◽  
Average Position

Tactic learning in virtual reality (VR) has been proven to be effective for basketball training. Endowed with the ability of generating virtual defenders in real time according to the movement of virtual offenders controlled by the user, a VR basketball training system can bring more immersive and realistic experiences for the trainee. In this article, an autoregressive generative model for instantly producing basketball defensive trajectory is introduced. We further focus on the issue of preserving the diversity of the generated trajectories. A differentiable sampling mechanism is adopted to learn the continuous Gaussian distribution of player position. Moreover, several heuristic loss functions based on the domain knowledge of basketball are designed to make the generated trajectories assemble real situations in basketball games. We compare the proposed method with the state-of-the-art works in terms of both objective and subjective manners. The objective manner compares the average position, velocity, and acceleration of the generated defensive trajectories with the real ones to evaluate the fidelity of the results. In addition, more high-level aspects such as the empty space for offender and the defensive pressure of the generated trajectory are also considered in the objective evaluation. As for the subjective manner, visual comparison questionnaires on the proposed and other methods are thoroughly conducted. The experimental results show that the proposed method can achieve better performance than previous basketball defensive trajectory generation works in terms of different evaluation metrics.

Integration of Symbolic Task Planning into Operations within an Unstructured Environment

2012 ◽  
Vol 2 (3) ◽  
pp. 38-57 ◽  
Author(s):  
Renxi Qiu ◽  
Alexandre Noyvirt ◽  
Ze Ji ◽  
Anthony Soroka ◽  
Dayou Li ◽  
...  
Keyword(s):  
Real Time ◽  
Time Perception ◽  
Domain Knowledge ◽  
Task Planning ◽  
Task Execution ◽  
Unstructured Environments ◽  
Planning Approach ◽  
The World ◽  
Unstructured Environment ◽  
High Level

To ensure a robot capable of robust task execution in unstructured environments, task planners need to have a high-level understanding of the nature of the world, reasoning for deliberate actions, and reacting to environment changes. Proposed is a practical task planning approach that seamlessly integrating deeper domain knowledge, real time perception and symbolic planning for robot operation. A higher degree of autonomy under unstructured environment will be endowed to the robot with the proposed approach.

Sign in / Sign up

Export Citation Format

Share Document