Whole-Part Relations Rule-Based Automatic Identification: Issues from Fine-Grained Error Analysis

2014 ◽  
pp. 37-50
Author(s):  
Ilia Markov ◽  
Nuno Mamede ◽  
Jorge Baptista
Keyword(s):  
Error Analysis ◽  
Automatic Identification ◽  
Rule Based ◽  
Fine Grained

scholarly journals Coconuts and curtain cakes: The production of wh-questions in ASD

2021 ◽  
Vol 6 ◽  
pp. 239694152098295
Author(s):  
Nufar Sukenik ◽  
Eléonore Morin ◽  
Naama Friedmann ◽  
Philippe Prevost ◽  
Laurice Tuller
Keyword(s):  
Error Analysis ◽  
Language Disorder ◽  
Syntactic Structure ◽  
Autism Spectrum ◽  
Production Rates ◽  
Fine Grained ◽  
Depth Analysis ◽  
Wh Questions ◽  
Analysis Of Error ◽  
Error Types

Background and aims Children with autism spectrum disorders (ASD) have been found to exhibit difficulties in wh-question production. It is unclear whether these difficulties are pragmatic or syntactic in nature. The current study used a question elicitation task to assess the production of subject and object wh-questions of children with ASD in two different languages (Hebrew and French) wherein the syntactic structure of wh-questions is different, a fact that may contribute to better understanding of the underlying deficits affecting wh-question production. Crucially, beyond the general correct/error rate we also performed an in-depth analysis of error types, comparing syntactic to pragmatic errors and comparing the distribution of errors in the ASD group to that of children with typical development (TD) and children with Developmental Language Disorder (DLD). Results Correct production rates were found to be similar for the ASD and DLD groups, but error analysis revealed important differences between the ASD groups in the two languages and the DLD group. The Hebrew- and French ASD groups were found to produce pragmatic errors, which were not found in children with DLD. The pragmatic errors were similar in the two ASD groups. Syntactic errors were affected by the structure of each language. Conclusions Our results have shown that although the two ASD groups come from different countries and speak different languages, the correct production rates and more importantly, the error types were very similar in the two ASD groups, and very different compared to TD children and children with DLD. Implications: Our results highlight the importance of creating research tasks that test different linguistic functions independently and strengthen the need for conducting fine-grained error analysis to differentiate between groups and gain insights into the deficits underlying each of them.

Automatic Identification of Unique Conference Names using Rule-based System

2020 ◽  
Author(s):  
Nuttapol Boonsom ◽  
Suwimol Wahakit ◽  
Thearith Ponn ◽  
Worapan Kusakunniran ◽  
Kittikhun Thongkanchorn
Keyword(s):  
Automatic Identification ◽  
Rule Based ◽  
Rule Based System

scholarly journals Automatic identification of time series features for rule-based forecasting

2001 ◽  
Vol 17 (2) ◽  
pp. 143-157 ◽  
Author(s):  
Monica Adya ◽  
Fred Collopy ◽  
J.Scott Armstrong ◽  
Miles Kennedy
Keyword(s):  
Time Series ◽  
Automatic Identification ◽  
Rule Based

scholarly journals SBML Level 3 package: Multistate, Multicomponent and Multicompartment Species, Version 1, Release 1

2018 ◽  
Vol 15 (1) ◽  
Author(s):  
Fengkai Zhang ◽  
Martin Meier-Schellersheim
Keyword(s):  
Plasma Membrane ◽  
Spatial Information ◽  
Reaction Networks ◽  
Rule Based ◽  
Fine Grained ◽  
Multiple Components ◽  
Level 3 ◽  
Type Concept ◽  
Definition Of ◽  
Molecular Components

AbstractRule-based modeling is an approach that permits constructing reaction networks based on the specification of rules for molecular interactions and transformations. These rules can encompass details such as the interacting sub-molecular domains (components) and the states such as phosphorylation and binding status of the involved components. Fine-grained spatial information such as the locations of the molecular components relative to a membrane (e.g. whether a modeled molecular domain is embedded into the inner leaflet of the cellular plasma membrane) can also be provided. Through wildcards representing component states entire families of molecule complexes sharing certain properties can be specified as patterns. This can significantly simplify the definition of models involving species with multiple components, multiple states and multiple compartments. The SBML Level 3 Multi Package (Multistate, Multicomponent and Multicompartment Species Package for SBML Level 3) extends the SBML Level 3 core with the “type” concept in the Species and Compartment classes and therefore reaction rules may contain species that can be patterns and be in multiple locations in reaction rules. Multiple software tools such as Simmune and BioNetGen support the SBML Level 3 Multi package that thus also becomes a medium for exchanging rule-based models.

scholarly journals An Anomaly Detection Method for AIS Trajectory Based on Kinematic Interpolation

2021 ◽  
Vol 9 (6) ◽  
pp. 609
Author(s):  
Shaoqing Guo ◽  
Junmin Mou ◽  
Linying Chen ◽  
Pengfei Chen
Keyword(s):  
Anomaly Detection ◽  
Detection Method ◽  
Industrial Applications ◽  
Automatic Identification ◽  
Identification System ◽  
Maritime Transport ◽  
Rule Based ◽  
Enormous Amount ◽  
Kinematic Information

With the enormous amount of information provided by the ship Automatic Identification System (AIS), AIS is now playing a significant role in maritime transport system-related research and development. Many kinds of research and industrial applications are based on the ship trajectory extracted from raw AIS data. However, due to the issues of equipment, the transmission environment, and human factors, the raw AIS data inevitably contain abnormal messages, which have hindered the utilization of such information in practice. Thus, in this paper, an anomaly detection method that focuses on AIS trajectory is proposed, making comprehensive use of the kinematic information of the ship in the AIS data. The method employs three steps to obtain non-error AIS trajectories: (1) data preprocessing, (2) kinematic estimation, and (3) error clustering. It should be noted that steps (2) and (3) are involved in an iterative process to determine all of the abnormal data. A case study is then conducted to test the proposed method on real-world AIS data, followed by a comparison between the proposed method and the rule-based anomaly detection method. As the processed trajectories show fewer abnormal features, the results indicate that the method improves performance and can accurately detect as much abnormal data as possible.

Risk Perception Oriented Autonomous Ship Navigation in AIS Environment

2020 ◽  
Author(s):  
Ruolan Zhang ◽  
Masao Furusho
Keyword(s):  
Decision Making ◽  
Reinforcement Learning ◽  
Risk Perception ◽  
Adaptive Systems ◽  
Learning Algorithm ◽  
Automatic Identification ◽  
Identification System ◽  
Rule Based ◽  
Ship Navigation ◽  
Frame Motion

Abstract Due to the quality and error of the data itself, historical automatic identification system (AIS) data was insufficient used to predict navigation risk at sea, but it adequately used to train decision-making neural networks. This paper presents a real AIS ship navigation environment with a rule-based and a neural-based decision processes with frame motion and training the decision network using a deep reinforcement learning algorithm. Rule-based decision-making has several applications in the field of adaptive systems, expert systems, and decision support systems, it also including general ship navigation which regulated by the convention on the international regulations for preventing collisions at sea (COLREGs). However, if someone intend to achieve full unmanned ship navigation without any remote control at the open sea, a rule-based decision-making system cannot be implemented alone. With the growing amount of data, complex sea environment, different collision scenarios, the agent-based decision has become an important role in transportation. For ships, combined rule-based and neural-based decision-making is the only option. It has become progressively challenging to satisfy autonomous decision-making development requirements. This study uses deep reinforcement learning to evaluate the performance of decision-making efficiency under different AIS data input shapes. The results show that the decision neural network trained with AIS data has good robustness and a high ability to achieve collision avoidance. Furthermore, using the same methodology, include instructive guidance for processing radar, camera, ENC, etc., respond to different risk perception tasks in different scenarios. It has important implications for fully unmanned navigation.

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