Compound or phrase or in between? Testing linguistic criteria for compoundhood in English

2020 ◽  
Vol 13 (2) ◽  
pp. 250-281
Author(s):  
Patrick Ziering ◽  
Lonneke van der Plas
Keyword(s):  
Machine Learning ◽  
Empirical Study ◽  
European Parliament ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
Nominal Compounds ◽  
Definition Of

In this paper, we present an empirical study on the definition of compounds in English, the graded nature of the phenomenon and its correlations with the commonly used linguistic criteria for compoundhood. We create a resource that includes a diverse set of nominal compounds identified by two trained independent annotators in sentences from the proceedings of the European Parliament. In addition, the annotators provide ratings on the compoundhood of the identified compounds, and ratings for the applicability of six prominent linguistic criteria of compoundhood for each item. We show the controversy of defining compounds in practice by comparing the annotations of two annotators, and the graded nature of compoundhood. By measuring the correlation between compoundhood and the six diverse linguistic criteria using machine learning techniques, we show that some linguistic criteria are stronger predictors of compoundhood than others.

scholarly journals Redefining Safety in Light of Human-Robot Interaction: A Critical Review of Current Standards and Regulations

2021 ◽  
Vol 3 ◽  
Author(s):  
Alberto Martinetti ◽  
Peter K. Chemweno ◽  
Kostas Nizamis ◽  
Eduard Fosch-Villaronga
Keyword(s):  
Machine Learning ◽  
Human Robot Interaction ◽  
Cloud Services ◽  
Machine Learning Techniques ◽  
Physical Interaction ◽  
Robot Interaction ◽  
Learning Techniques ◽  
Standards And Regulations ◽  
Physical Impact ◽  
Definition Of

Policymakers need to consider the impacts that robots and artificial intelligence (AI) technologies have on humans beyond physical safety. Traditionally, the definition of safety has been interpreted to exclusively apply to risks that have a physical impact on persons’ safety, such as, among others, mechanical or chemical risks. However, the current understanding is that the integration of AI in cyber-physical systems such as robots, thus increasing interconnectivity with several devices and cloud services, and influencing the growing human-robot interaction challenges how safety is currently conceptualised rather narrowly. Thus, to address safety comprehensively, AI demands a broader understanding of safety, extending beyond physical interaction, but covering aspects such as cybersecurity, and mental health. Moreover, the expanding use of machine learning techniques will more frequently demand evolving safety mechanisms to safeguard the substantial modifications taking place over time as robots embed more AI features. In this sense, our contribution brings forward the different dimensions of the concept of safety, including interaction (physical and social), psychosocial, cybersecurity, temporal, and societal. These dimensions aim to help policy and standard makers redefine the concept of safety in light of robots and AI’s increasing capabilities, including human-robot interactions, cybersecurity, and machine learning.

Improving Automated Planning with Machine Learning

2010 ◽  
pp. 599-620
Author(s):  
Susana Fernández Arregui ◽  
Sergio Jiménez Celorrio ◽  
Tomás de la Rosa Turbides
Keyword(s):  
Machine Learning ◽  
Automated Planning ◽  
Machine Learning Techniques ◽  
Advantages And Disadvantages ◽  
Learning Techniques ◽  
Depth Analysis ◽  
Search Processes ◽  
Action Models ◽  
New Challenges ◽  
Definition Of

This chapter reports the last machine learning techniques for the assistance of automated planning. Recent discoveries in automated planning have opened the scope of planners, from toy problems to real-world applications, making new challenges come into focus. The planning community believes that machine learning can assist to address these new challenges. The chapter collects the last machine learning techniques for assisting automated planners classified in: techniques for the improvement of the planning search processes and techniques for the automatic definition of planning action models. For each technique, the chapter provides an in-depth analysis of their domain, advantages and disadvantages. Finally, the chapter draws the outline of the new promising avenues for research in learning for planning systems.

Improving Automated Planning with Machine Learning

2012 ◽  
pp. 1355-1373
Author(s):  
Susana Fernández Arregui ◽  
Sergio Jiménez Celorrio ◽  
Tomás de la Rosa Turbides
Keyword(s):  
Machine Learning ◽  
Automated Planning ◽  
Machine Learning Techniques ◽  
Advantages And Disadvantages ◽  
Learning Techniques ◽  
Depth Analysis ◽  
Search Processes ◽  
Action Models ◽  
New Challenges ◽  
Definition Of

This chapter reports the last machine learning techniques for the assistance of automated planning. Recent discoveries in automated planning have opened the scope of planners, from toy problems to real-world applications, making new challenges come into focus. The planning community believes that machine learning can assist to address these new challenges. The chapter collects the last machine learning techniques for assisting automated planners classified in: techniques for the improvement of the planning search processes and techniques for the automatic definition of planning action models. For each technique, the chapter provides an in-depth analysis of their domain, advantages and disadvantages. Finally, the chapter draws the outline of the new promising avenues for research in learning for planning systems.

scholarly journals MQTTset, a New Dataset for Machine Learning Techniques on MQTT

Sensors ◽  
2020 ◽  
Vol 20 (22) ◽  
pp. 6578
Author(s):  
Ivan Vaccari ◽  
Giovanni Chiola ◽  
Maurizio Aiello ◽  
Maurizio Mongelli ◽  
Enrico Cambiaso
Keyword(s):  
Machine Learning ◽  
Cyber Security ◽  
Detection System ◽  
Critical Issue ◽  
Cyber Attacks ◽  
Machine Learning Techniques ◽  
Detection Systems ◽  
Learning Techniques ◽  
Iot Devices ◽  
Definition Of

IoT networks are increasingly popular nowadays to monitor critical environments of different nature, significantly increasing the amount of data exchanged. Due to the huge number of connected IoT devices, security of such networks and devices is therefore a critical issue. Detection systems assume a crucial role in the cyber-security field: based on innovative algorithms such as machine learning, they are able to identify or predict cyber-attacks, hence to protect the underlying system. Nevertheless, specific datasets are required to train detection models. In this work we present MQTTset, a dataset focused on the MQTT protocol, widely adopted in IoT networks. We present the creation of the dataset, also validating it through the definition of a hypothetical detection system, by combining the legitimate dataset with cyber-attacks against the MQTT network. Obtained results demonstrate how MQTTset can be used to train machine learning models to implement detection systems able to protect IoT contexts.

scholarly journals The Scientific Study of Passive Thinking: The Methodology of Mind Wandering Research

2020 ◽  
Author(s):  
Samuel Murray ◽  
Zachary Irving ◽  
Kristina Krasich
Keyword(s):  
Machine Learning ◽  
Empirical Study ◽  
Eye Tracking ◽  
Mind Wandering ◽  
Self Report ◽  
Machine Learning Techniques ◽  
Objective Measures ◽  
Learning Techniques ◽  
Self Reports ◽  
Direct Sensitivity

In this chapter, we survey methodological challenges in the empirical study of mind wandering and provide a metaphysical framework that begins to address these challenges. We argue that mind wandering is a passive manifestation of agency—passive because people cannot mind wander on command and a manifestation of agency because the onset, progression, and content of mind wandering often exhibits direct sensitivity to personal concerns and plans. To measure passive thinking, researchers must ask, “Is your mind wandering?” Worries about this self-report methodology have encouraged researchers to develop “objective” measures of mind wandering through eye tracking and machine learning techniques. These “objective” measures, however, are validated in terms of how well they predict self-reports, which means that purportedly objective measures of mind wandering retain a subjective core. To assuage worries about self-report (and, ultimately, vindicate objective measures of mind wandering), we offer a metaphysical account of mind wandering that generates several predictions about its causes and consequences. This account also justifies different methods for measuring mind wandering.

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